Causality and Treatment Effects
|
|
|
- Johnathan Gibbs
- 5 years ago
- Views:
Transcription
1 Causality and Treatment Effects Prof. Jacob M. Montgomery Quantitative Political Methodology (L32 363) October 24, 2016 Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
2 Causal inference Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
3 Overview Thinking about causality Average treatment effects Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
4 We do not live in a univariate world So far, we ve done univariate inference. Moving forward we will do bivariate inference. This is useful: With the right study design (e.g., experimental research) Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
5 We do not live in a univariate world So far, we ve done univariate inference. Moving forward we will do bivariate inference. This is useful: With the right study design (e.g., experimental research) Why? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
6 We do not live in a univariate world So far, we ve done univariate inference. Moving forward we will do bivariate inference. This is useful: With the right study design (e.g., experimental research) Why? This is a powerful, powerful way to do research Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
7 We do not live in a univariate world So far, we ve done univariate inference. Moving forward we will do bivariate inference. This is useful: With the right study design (e.g., experimental research) Why? This is a powerful, powerful way to do research Building block for more advanced models Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
8 We do not live in a univariate world So far, we ve done univariate inference. Moving forward we will do bivariate inference. This is useful: With the right study design (e.g., experimental research) Why? This is a powerful, powerful way to do research Building block for more advanced models Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
9 We do not live in a univariate world So far, we ve done univariate inference. Moving forward we will do bivariate inference. This is useful: With the right study design (e.g., experimental research) Why? This is a powerful, powerful way to do research Building block for more advanced models NOTE: In many situations (e.g., civil war), we don t have experimental data. This means we need to think about all relevant predictors and control variables. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
10 We do not live in a univariate world So far, we ve done univariate inference. Moving forward we will do bivariate inference. This is useful: With the right study design (e.g., experimental research) Why? This is a powerful, powerful way to do research Building block for more advanced models NOTE: In many situations (e.g., civil war), we don t have experimental data. This means we need to think about all relevant predictors and control variables. We will finish the semester by talking about multiple regression as one method for attempting to deal with this. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
11 Causality In political science we want to make causal claims. X Y What does this mean? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
12 Causality In political science we want to make causal claims. X Y What does this mean? Let s do this a bit more formally for the case of an experiment (the easiest way to think about it). Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
13 Causal inference We will use T to represent a treatment variable. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
14 Causal inference We will use T to represent a treatment variable. For a categorical treatment T i = 1 if unit i receives the treatment 0 if unit i receives the control Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
15 Causal inference We will use T to represent a treatment variable. For a categorical treatment T i = 1 if unit i receives the treatment 0 if unit i receives the control We let yi 1 represent the outcome of the ith unit if the treatment is given. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
16 Causal inference We will use T to represent a treatment variable. For a categorical treatment T i = 1 if unit i receives the treatment 0 if unit i receives the control We let yi 1 represent the outcome of the ith unit if the treatment is given. We let y 0 i represent the outcome of the ith unit if the control is given. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
17 Causal inference We will use T to represent a treatment variable. For a categorical treatment T i = 1 if unit i receives the treatment 0 if unit i receives the control We let yi 1 represent the outcome of the ith unit if the treatment is given. We let yi 0 represent the outcome of the ith unit if the control is given. One of these is observed, the other is the counterfactual what would have been observed if the other treatment have been given? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
18 Causal inference We will use T to represent a treatment variable. For a categorical treatment T i = 1 if unit i receives the treatment 0 if unit i receives the control We let yi 1 represent the outcome of the ith unit if the treatment is given. We let yi 0 represent the outcome of the ith unit if the control is given. One of these is observed, the other is the counterfactual what would have been observed if the other treatment have been given? The causal effect of T i will then be yi 1 yi 0 Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
19 Causal inference We will use T to represent a treatment variable. For a categorical treatment T i = 1 if unit i receives the treatment 0 if unit i receives the control We let yi 1 represent the outcome of the ith unit if the treatment is given. We let yi 0 represent the outcome of the ith unit if the control is given. One of these is observed, the other is the counterfactual what would have been observed if the other treatment have been given? The causal effect of T i will then be yi 1 yi 0 Ex., My theory is that individuals who watched this TV ad will be more likely to vote for Ted Cruz than if they didn t watch it. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
20 Average treatment effects We cannot measure individual level causal effects Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
21 Average treatment effects We cannot measure individual level causal effects We can estimate the population average treatment effect by looking at those who received the treatment and those who did not. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
22 Average treatment effects We cannot measure individual level causal effects We can estimate the population average treatment effect by looking at those who received the treatment and those who did not. ATE = mean(yi 1 yi 0) Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
23 Average treatment effects We cannot measure individual level causal effects We can estimate the population average treatment effect by looking at those who received the treatment and those who did not. ATE = mean(yi 1 yi 0) ATE = mean(yi 1) mean(y i 0) Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
24 Average treatment effects We cannot measure individual level causal effects We can estimate the population average treatment effect by looking at those who received the treatment and those who did not. ATE = mean(yi 1 yi 0) ATE = mean(yi 1) mean(y i 0) Each group acts as a counterfactual for the other Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
25 Average treatment effects We cannot measure individual level causal effects We can estimate the population average treatment effect by looking at those who received the treatment and those who did not. ATE = mean(yi 1 yi 0) ATE = mean(yi 1) mean(y i 0) Each group acts as a counterfactual for the other Ex., My theory is that those individuals who watched this TV ad will be more likely to vote for Mitt Romney on average than those who didn t watch it. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
26 The fundamental problem of causal inference The fundamental problem of causal inference is that at most one of yi 0 and yi 1 can be observed. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
27 The fundamental problem of causal inference The fundamental problem of causal inference is that at most one of yi 0 and yi 1 can be observed. We can think of each of these as potential outcomes. However, we can only observe one. The other is the counterfactual. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
28 The fundamental problem of causal inference The fundamental problem of causal inference is that at most one of yi 0 and yi 1 can be observed. We can think of each of these as potential outcomes. However, we can only observe one. The other is the counterfactual. Estimation of causal effects requires some combination of: Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
29 The fundamental problem of causal inference The fundamental problem of causal inference is that at most one of yi 0 and yi 1 can be observed. We can think of each of these as potential outcomes. However, we can only observe one. The other is the counterfactual. Estimation of causal effects requires some combination of: certain research designs that approximate potential outcomes Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
30 The fundamental problem of causal inference The fundamental problem of causal inference is that at most one of yi 0 and yi 1 can be observed. We can think of each of these as potential outcomes. However, we can only observe one. The other is the counterfactual. Estimation of causal effects requires some combination of: certain research designs that approximate potential outcomes randomization Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
31 The fundamental problem of causal inference The fundamental problem of causal inference is that at most one of yi 0 and yi 1 can be observed. We can think of each of these as potential outcomes. However, we can only observe one. The other is the counterfactual. Estimation of causal effects requires some combination of: certain research designs that approximate potential outcomes randomization statistical adjustment Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
32 Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
33 Stand up if your student ID ends in: Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
34 Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
35 Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
36 Take away Causal effects rely on unobserved counterfactuals Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
37 Take away Causal effects rely on unobserved counterfactuals At best, we can estimate average treatment effects comparing those who received a treatment with those who don t. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
38 Take away Causal effects rely on unobserved counterfactuals At best, we can estimate average treatment effects comparing those who received a treatment with those who don t. In order for this to work, each group must be identical (on average) in every way except the treatment. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
39 Take away Causal effects rely on unobserved counterfactuals At best, we can estimate average treatment effects comparing those who received a treatment with those who don t. In order for this to work, each group must be identical (on average) in every way except the treatment. The best way to achieve this is through random assignment (i.e., experiments) Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
40 Take away Causal effects rely on unobserved counterfactuals At best, we can estimate average treatment effects comparing those who received a treatment with those who don t. In order for this to work, each group must be identical (on average) in every way except the treatment. The best way to achieve this is through random assignment (i.e., experiments) What if this assumption is not met? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
41 Confounders and causality PROBLEM: This only works if the two groups are, on average, otherwise identical Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
42 Confounders and causality PROBLEM: This only works if the two groups are, on average, otherwise identical If the two groups differ on other factors that also cause yi 1 and y 0 this is a confounding relationship. i, Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
43 Confounders and causality PROBLEM: This only works if the two groups are, on average, otherwise identical If the two groups differ on other factors that also cause yi 1 and y 0 this is a confounding relationship. If this is the case, our counterfactual is wrong and we can make no causal claim. i, Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
44 Confounders and causality PROBLEM: This only works if the two groups are, on average, otherwise identical If the two groups differ on other factors that also cause yi 1 and y 0 this is a confounding relationship. If this is the case, our counterfactual is wrong and we can make no causal claim. i, Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
45 Confounders and causality PROBLEM: This only works if the two groups are, on average, otherwise identical If the two groups differ on other factors that also cause yi 1 and y 0 this is a confounding relationship. If this is the case, our counterfactual is wrong and we can make no causal claim. Take Away If you aren t controlling for all other relevant variables (through randomization or statistical methods), you cannot make a valid causal claim. i, Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
46 Thinking about confounding variables Direct causal relationships: X 1 Y Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
47 Thinking about confounding variables Direct causal relationships: Spurious relationships: X 1 Y Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
48 Thinking about confounding variables Direct causal relationships: Spurious relationships: X 1 Y X 2 X 1 AND X 2 Y 2 Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
49 Thinking about confounding variables Direct causal relationships: Spurious relationships: X 1 Y Chain relationships: X 2 X 1 AND X 2 Y 2 Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
50 Thinking about confounding variables Direct causal relationships: Spurious relationships: X 1 Y Chain relationships: X 2 X 1 AND X 2 Y 2 X 1 X 2 Y Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
51 Thinking about confounding variables Direct causal relationships: Spurious relationships: X 1 Y Chain relationships: Multiple causation: X 2 X 1 AND X 2 Y 2 X 1 X 2 Y X 1 Y AND X 2 Y Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
52 Thinking about confounding variables Direct causal relationships: Spurious relationships: X 1 Y Chain relationships: Multiple causation: X 2 X 1 AND X 2 Y 2 X 1 X 2 Y X 1 Y AND X 2 Y Direct and indirect causation: X 1 Y AND X 1 X 2 AND X 2 Y Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
53 Write down one of each type of claim for this data. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
54 Being a responsible causal analyst Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
55 Less silly examples Vitamin C? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
56 Less silly examples Vitamin C? Flossing? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
57 Less silly examples Vitamin C? Flossing? Drinking while pregnant? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
58 Less silly examples Vitamin C? Flossing? Drinking while pregnant? Fox News? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
59 At a minimum we need to show... Association What we will be doing this rest of the semester Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
60 At a minimum we need to show... Association What we will be doing this rest of the semester Correlation, contingency tables, regression coefficients,... Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
61 At a minimum we need to show... Association What we will be doing this rest of the semester Correlation, contingency tables, regression coefficients,... Association = causation Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
62 At a minimum we need to show... Association What we will be doing this rest of the semester Correlation, contingency tables, regression coefficients,... Association = causation Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
63 At a minimum we need to show... Association What we will be doing this rest of the semester Correlation, contingency tables, regression coefficients,... Association = causation Temporal order For T i to cause Y i it must come before Y in time order Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
64 At a minimum we need to show... Association What we will be doing this rest of the semester Correlation, contingency tables, regression coefficients,... Association = causation Temporal order For T i to cause Y i it must come before Y in time order Post hoc ergo propter hoc Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
65 At a minimum we need to show... Association What we will be doing this rest of the semester Correlation, contingency tables, regression coefficients,... Association = causation Temporal order For T i to cause Y i it must come before Y in time order Post hoc ergo propter hoc After this, therefore because of this Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
66 At a minimum we need to show... Association What we will be doing this rest of the semester Correlation, contingency tables, regression coefficients,... Association = causation Temporal order For T i to cause Y i it must come before Y in time order Post hoc ergo propter hoc After this, therefore because of this Temporal order does = causation (e.g., every superstition ever) Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
67 At a minimum we need to show... Eliminate alternative explanations Suppose there is an association and a proper time order. We stil cannot infer causation. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
68 At a minimum we need to show... Eliminate alternative explanations Suppose there is an association and a proper time order. We stil cannot infer causation. Rather, we must test for all alternative explanations. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
69 At a minimum we need to show... Eliminate alternative explanations Suppose there is an association and a proper time order. We stil cannot infer causation. Rather, we must test for all alternative explanations. Only if all of these have been resolved can we claim causation. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
70 At a minimum we need to show... Eliminate alternative explanations Suppose there is an association and a proper time order. We stil cannot infer causation. Rather, we must test for all alternative explanations. Only if all of these have been resolved can we claim causation. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
71 At a minimum we need to show... Eliminate alternative explanations Suppose there is an association and a proper time order. We stil cannot infer causation. Rather, we must test for all alternative explanations. Only if all of these have been resolved can we claim causation. How can we do this? Experimental control Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
72 At a minimum we need to show... Eliminate alternative explanations Suppose there is an association and a proper time order. We stil cannot infer causation. Rather, we must test for all alternative explanations. Only if all of these have been resolved can we claim causation. How can we do this? Experimental control Statistical control (Stay tuned...) Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
73 Problem: It can be subtle (Mount 2010) Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
74 A case study Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
75 Problem: It can be subtle Rogers, Coffman, and Bergman: While the authors control for certain variables, their research only implies there is a relationship between parental involvement and student performance. This caveat is important; the existence of a relationship does not tell us what causes what. Think of it this way: If you had two children, and one was getting A s and the other C s, which of them would you help more? The C student. An outsider, noticing that you ve spent the school year helping only one of your children, might infer that parental help caused that child to earn lower grades. This of course would not be the case, and inferring causation here would be a mistake. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
76 If you see a surprising result, be skeptical Occasional Notes 35 Nobel Laureates per 10 Million Population r=0.791 P< The Netherlands Poland Portugal Greece Japan China Brazil Belgium Canada Italy Spain United States France Australia Sweden Austria Denmark Finland Ireland Norway United Kingdom Switzerland Germany Chocolate Consumption (kg/yr/capita) Figure 1. Correlation between Countries Annual Per Capita Chocolate Consumption and the Number of Nobel Laureates per 10 Million Population. Discussion about 14 Nobel laureates, yet we observe 32. Lecture 13 (QPM 2016) Causality and Treatment Considering Effects that in this instance the October observed 24, / 32
77 Example: Can social pressure increase turnout? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
78 Comparing two populations We have two independent samples, and we want to compare them. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
79 Comparing two populations We have two independent samples, and we want to compare them. Our data will look like this. Variable 1 Variable 2 (Outcome or response) (Explanatory or grouping) Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
80 Example: Social Pressure and Turnout Gerber, A., Green, D., and Larimer C.W Social Pressure and Voter Turnout: Evidence from a Large-Scale Field Experiment. American Political Science Review 101(1): Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
81 Example: Social Pressure and Turnout Gerber, A., Green, D., and Larimer C.W Social Pressure and Voter Turnout: Evidence from a Large-Scale Field Experiment. American Political Science Review 101(1): Obviously, most of these treatments worked. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
82 Example: Social Pressure and Turnout Gerber, A., Green, D., and Larimer C.W Social Pressure and Voter Turnout: Evidence from a Large-Scale Field Experiment. American Political Science Review 101(1): Obviously, most of these treatments worked. (Note: The analysis of this experiment is actually somewhat more complicated than this due to the way individuals were assigned to treatment groups.) Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
83 Do Politicians Racially Discriminate? Broad Question Is racial discrimination against blacks still a problem in the political sphere? Puzzle Do legislators discriminate against individual requests for constituency service on the basis of race? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
84 Tuesday, October 25, 2011 Tuesday, October 25, 2011 Who do you think they are? Jake Mueller DeShawn Jackson Jake Mueller Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
85 Comparing legislators responsiveness Questions: Do legislators answer a higher proportion of s from the citizens they believe are white, Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
86 Comparing legislators responsiveness Questions: Do legislators answer a higher proportion of s from the citizens they believe are white,... even though both black & white don t signal party affiliation? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
87 Comparing legislators responsiveness Questions: Do legislators answer a higher proportion of s from the citizens they believe are white,... even though both black & white don t signal party affiliation?... even though both black & white signal being Republican? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
88 Comparing legislators responsiveness Questions: Do legislators answer a higher proportion of s from the citizens they believe are white,... even though both black & white don t signal party affiliation?... even though both black & white signal being Republican?... even though both black & white signal being Democrat? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
89 Comparing legislators responsiveness Questions: Do legislators answer a higher proportion of s from the citizens they believe are white,... even though both black & white don t signal party affiliation?... even though both black & white signal being Republican?... even though both black & white signal being Democrat? Experiment Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
90 Comparing legislators responsiveness Questions: Do legislators answer a higher proportion of s from the citizens they believe are white,... even though both black & white don t signal party affiliation?... even though both black & white signal being Republican?... even though both black & white signal being Democrat? Experiment The sample includes states legislators in 44 U.S. states with a valid address in September Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
91 Comparing legislators responsiveness Questions: Do legislators answer a higher proportion of s from the citizens they believe are white,... even though both black & white don t signal party affiliation?... even though both black & white signal being Republican?... even though both black & white signal being Democrat? Experiment The sample includes states legislators in 44 U.S. states with a valid address in September Race was signaled by randomizing whether the was signed and sent from an account with the name Jake Mueller or DeShawn Jackson. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
92 Comparing legislators responsiveness Questions: Do legislators answer a higher proportion of s from the citizens they believe are white,... even though both black & white don t signal party affiliation?... even though both black & white signal being Republican?... even though both black & white signal being Democrat? Experiment The sample includes states legislators in 44 U.S. states with a valid address in September Race was signaled by randomizing whether the was signed and sent from an account with the name Jake Mueller or DeShawn Jackson. The text of the was also manipulated so as to signal the partisan preference of the sender. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
93 Comparing legislators responsiveness Questions: Do legislators answer a higher proportion of s from the citizens they believe are white,... even though both black & white don t signal party affiliation?... even though both black & white signal being Republican?... even though both black & white signal being Democrat? Experiment The sample includes states legislators in 44 U.S. states with a valid address in September Race was signaled by randomizing whether the was signed and sent from an account with the name Jake Mueller or DeShawn Jackson. The text of the was also manipulated so as to signal the partisan preference of the sender. The cross-tabulation between race & partisan preference gives six treatments (or groups). Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
94 Comparing legislators responsiveness Questions: Do legislators answer a higher proportion of s from the citizens they believe are white,... even though both black & white don t signal party affiliation?... even though both black & white signal being Republican?... even though both black & white signal being Democrat? Experiment The sample includes states legislators in 44 U.S. states with a valid address in September Race was signaled by randomizing whether the was signed and sent from an account with the name Jake Mueller or DeShawn Jackson. The text of the was also manipulated so as to signal the partisan preference of the sender. The cross-tabulation between race & partisan preference gives six treatments (or groups). The outcome variable is the response (or lack thereof) to any s. Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
95 Results: Table 1?? Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
96 Class business Midterms PS posted today. Quiz for next class over t-tests Lecture 13 (QPM 2016) Causality and Treatment Effects October 24, / 32
Multivariate relationships. Week 6 22 February, 2016 Prof. Andrew Eggers
Multivariate relationships Week 6 22 February, 2016 Prof. Andrew Eggers 1 2 The new england journal of medicine occasional notes Chocolate Consumption, Cognitive Function, and Nobel Laureates Franz H.
STATS Relationships between variables: Correlation
STATS 1060 Relationships between variables: Correlation READINGS: Chapter 7 of your text book (DeVeaux, Vellman and Bock); on-line notes for correlation; on-line practice problems for correlation NOTICE:
WESTERN EUROPE PREVALENCE AND INCIDENCE OF PERIPHERAL ARTERY DISEASE AND CRITICAL LIMB ISCHEMIA 2017
WESTERN EUROPE PREVALENCE AND INCIDENCE OF PERIPHERAL ARTERY DISEASE AND CRITICAL LIMB ISCHEMIA 2017 Mary L. Yost 404-520-6652 , LLC 23 Ridge Rd. Beaufort, SC 20097 Copyright Pending 2017 All rights reserved,
Political Science 15, Winter 2014 Final Review
Political Science 15, Winter 2014 Final Review The major topics covered in class are listed below. You should also take a look at the readings listed on the class website. Studying Politics Scientifically
Case study examining the impact of German reunification on life expectancy
Supplementary Materials 2 Case study examining the impact of German reunification on life expectancy Table A1 summarises our case study. This is a simplified analysis for illustration only and does not
Global EHS Resource Center
Global EHS Resource Center Understand environmental and workplace safety requirements that affect your global operations. 800.372.1033 bna.com/gelw Global EHS Resource Center This comprehensive research
Chapter Eight: Multivariate Analysis
Chapter Eight: Multivariate Analysis Up until now, we have covered univariate ( one variable ) analysis and bivariate ( two variables ) analysis. We can also measure the simultaneous effects of two or
Chapter Eight: Multivariate Analysis
Chapter Eight: Multivariate Analysis Up until now, we have covered univariate ( one variable ) analysis and bivariate ( two variables ) analysis. We can also measure the simultaneous effects of two or
The influence of societal individualism on a century of tobacco use: modelling the prevalence of smoking Appendices A and B
The influence of societal individualism on a century of tobacco use: modelling the prevalence of smoking Appendices A and B J.C. Lang, D.M. Abrams, and H. De Sterck A Additional Tables and Figures Table
Chapter 11 Nonexperimental Quantitative Research Steps in Nonexperimental Research
Chapter 11 Nonexperimental Quantitative Research (Reminder: Don t forget to utilize the concept maps and study questions as you study this and the other chapters.) Nonexperimental research is needed because
508 the number of suicide deaths in deaths per 100,000 people was the suicide rate in Suicide deaths in 2013 by gender
An overview of suicide statistics This document summarises information about suicide deaths in New Zealand covering up to 13. It does not attempt to explain causes of suicidal behaviour or causes of changes
Smokefree Policies in Europe: Are we there yet?
Smokefree Policies in Europe: Are we there yet? 14 April 2015, 9:00 10:30am Rue de l Industrie 24, 1040 Brussels Permanent Partners: Temporary Partners: The research for the SFP Smokefree Map was partially
PRODUCT INFORMATION. New Reagents for Dako CoverStainer. Choose the H&E staining intensity you want.
PRODUCT INFORMATION Primary Staining Dako CoverStainer Reagents New Reagents for Dako CoverStainer. Choose the H&E staining intensity you want. New flexible staining intensities to fit y Dako H&E Staining
Design and Analysis of a Cancer Prevention Trial: Plans and Results. Matthew Somerville 09 November 2009
Design and Analysis of a Cancer Prevention Trial: Plans and Results Matthew Somerville 09 November 2009 Overview Objective: Review the planned analyses for a large prostate cancer prevention study and
Bio-Rad Laboratories HEMOGLOBIN Testing Bio-Rad A1c. VARIANT II TURBO Link. Fully-Automated HbA 1c Testing
Bio-Rad Laboratories HEMOGLOBIN Testing Bio-Rad A1c VARIANT II TURBO Link Fully-Automated HbA 1c Testing Bio-Rad Laboratories HEMOGLOBIN Testing VARIANT II TURBO Link Efficient streamlined process Powerful
A. Indicate the best answer to each the following multiple-choice questions (20 points)
Phil 12 Fall 2012 Directions and Sample Questions for Final Exam Part I: Correlation A. Indicate the best answer to each the following multiple-choice questions (20 points) 1. Correlations are a) useful
Module 4 Introduction
Module 4 Introduction Recall the Big Picture: We begin a statistical investigation with a research question. The investigation proceeds with the following steps: Produce Data: Determine what to measure,
Deceased donation data in the UK. Paul Murphy National Clinical Lead for Organ Donation United Kingdom
Deceased donation data in the UK Paul Murphy National Clinical Lead for Organ Donation United Kingdom Deceased donation data in the UK And the story behind it Paul Murphy National Clinical Lead for Organ
Department of Paediatric Chemical Pathology and Neonatal Screening The Children s Hospital Sheffield S10 2TH UK. Annual Report 2002.
Ms M Downing Dr J R Bonham Professor R J Pollitt Telephone (+)44 114 271 7404 Fax (+)44 114 276 6205 Department of Paediatric Chemical Pathology and Neonatal Screening The Children s Hospital Sheffield
HRM Series PCB Power Relays
Features: SPCO contacts. Sealed to allow washing after flow soldering. Specifications: Coil Data: Nominal Voltage Nominal Power consumption : 3V dc to 24V dc. : 540mW to 720mW. Contact Data: Contact Arrangement
Type 1 Diabetes Australian Research Impact Analysis
Type 1 Diabetes Australian Research Impact Analysis Executive Overview Summary Type 1 diabetes research in Australia Australia is making a significant contribution to the quantity and quality of the global
WCPT COUNTRY PROFILE December 2017 SWEDEN
WCPT COUNTRY PROFILE December 2017 SWEDEN SWEDEN NUMBERS WCPT Members Practising physical therapists 11,043 Total number of physical therapist members in your member organisation 17,906 Total number of
The OECD Health Care Quality Indicators Project
The OECD Health Care Quality Indicators Project Ed Kelley, Ph.D. Head, OECD Health Care Quality Indicators Project Health Systems Working Party Luxembourg - April 26, 2005 1 Broad aims of the OECD s HCQI
PROMOTION AND PROTECTION OF ALL HUMAN RIGHTS, CIVIL, POLITICAL, ECONOMIC, SOCIAL AND CULTURAL RIGHTS, INCLUDING THE RIGHT TO DEVELOPMENT
UNITED NATIONS A General Assembly Distr. LIMITED A/HRC/11/L.16 16 June 2009 Original: ENGLISH HUMAN RIGHTS COUNCIL Eleventh session Agenda item 3 PROMOTION AND PROTECTION OF ALL HUMAN RIGHTS, CIVIL, POLITICAL,
(CORRELATIONAL DESIGN AND COMPARATIVE DESIGN)
UNIT 4 OTHER DESIGNS (CORRELATIONAL DESIGN AND COMPARATIVE DESIGN) Quasi Experimental Design Structure 4.0 Introduction 4.1 Objectives 4.2 Definition of Correlational Research Design 4.3 Types of Correlational
Allied Health: Sustainable Integrated Health Care for all Australians
Allied Health: Sustainable Integrated Health Care for all Australians Catherine Turnbull Chief Allied and Scientific Health Advisor SA Health Presentation to Indigenous Allied Health Australia Conference,
WCPT COUNTRY PROFILE December 2017 HUNGARY
WCPT COUNTRY PROFILE December 2017 HUNGARY HUNGARY NUMBERS WCPT Members Practising physical therapists 727 Total number of physical therapist members in your member organisation 4,000 Total number of practising
WCPT COUNTRY PROFILE December 2017 SERBIA
WCPT COUNTRY PROFILE December 2017 SERBIA SERBIA NUMBERS WCPT Members Practising physical therapists 622 Total number of physical therapist members in your member organisation 3,323 Total number of practising
Political Science 30: Political Inquiry Section 1
Political Science 30: Political Inquiry Section 1 Taylor Carlson tncarlson@ucsd.edu January 10, 2019 Carlson POLI 30-Section 1 January 10, 2019 1 / 16 Learning Outcomes By the end of section today, you
Bio-Rad Laboratories HEMOGLOBIN Testing Bio-Rad A1c. VARIANT II TURBO Link System. Fully-Automated HbA 1c Testing
Bio-Rad Laboratories HEMOGLOBIN Testing Bio-Rad A1c VARIANT II TURBO Link System Fully-Automated HbA 1c Testing Bio-Rad Laboratories HEMOGLOBIN Testing VARIANT II TURBO Link System Efficient streamlined
HPV, Cancer and the Vaccination Programme
HPV, Cancer and the Vaccination Programme Dr Brenda Corcoran Consultant in Public Health Medicine HSE National Immunisation Office What is HPV? Human papillomavirus Over 200 types identified Spread by
by EUA Denmark Italy Portugal Israel Canada Finland Luxembourg Spain Australia Mexico France Monaco Sweden China Austria Germany Netherlands Switzerland Japan Belgium Ireland Norway United Kingdom South
IOSH No Time to Lose campaign: working together to tackle asbestos-related cancer #NTTLasbestos. Jonathan Hughes IOSH Vice President
IOSH No Time to Lose campaign: working together to tackle asbestos-related cancer #NTTLasbestos Jonathan Hughes IOSH Vice President About the Institution of Occupational Safety and Health (IOSH) www.iosh.co.uk
Gender differences in competitive preferences: new cross-country empirical evidence
SCHUMPETER DISCUSSION PAPERS Gender differences in competitive preferences: new cross-country empirical evidence Werner Bönte SDP 2014-008 ISSN 1867-5352 by the author Gender differences in competitive
MENTAL HEALTH CARE. OECD HCQI Expert meeting 17 th of May, Rie Fujisawa
MENTAL HEALTH CARE OECD HCQI Expert meeting 17 th of May, 2013 Rie Fujisawa Mental health indicators Any hospital readmissions for patients with schizophrenia Same hospital readmissions for patients with
Burden and cost of alcohol, tobacco and illegal drugs globally and in Europe
Burden and cost of alcohol, tobacco and illegal drugs globally and in Europe Jürgen Rehm 1-4 Kevin D. Shield 1,2,3 1) Centre for Addiction and Mental Health, Toronto, Canada 2) University of Toronto, Canada
An analysis of structural changes in the consumption patterns of beer, wine and spirits applying an alternative methodology
An analysis of structural changes in the consumption patterns of beer, wine and spirits applying an alternative methodology 1. Introduction Jan BENTZEN, Valdemar SMITH Department of Economics and Business
10. Introduction to Multivariate Relationships
10. Introduction to Multivariate Relationships Bivariate analyses are informative, but we usually need to take into account many variables. Many explanatory variables have an influence on any particular
Unit 7 Comparisons and Relationships
Unit 7 Comparisons and Relationships Objectives: To understand the distinction between making a comparison and describing a relationship To select appropriate graphical displays for making comparisons
Men & Health Work. Difference can make a difference Steve Boorman & Ian Banks RSPH Academy 2013
Men & Health Promotion @ Work Difference can make a difference Steve Boorman & Ian Banks RSPH Academy 2013 Difference can make a Difference Mens health: State of mens health Use of services Role of the
Louisville '19 Attachment #69
Telephone Meeting Approved and why I propose Using zoom to fulfill both Phone and Virtual video meeting Formats. The first established phone meeting Sanctioned by Gamblers Anonymous (listed on Trustee
The health economic landscape of cancer in Europe
1 Approval number The health economic landscape of cancer in Europe Bengt Jönsson, Professor Emeritus of Health Economics Stockholm School of Economics 2 Disclaimer This presentation was developed by Professor
Immunohematology. IH-QC Modular System. Select. Combine. Control.
Immunohematology IH-QC Modular System Select. Combine. Control. IH-QC Modular System Select. Combine. Control. Transfusion guidelines recommend regular checking of test materials, test methods, local working
Chapter 4: More about Relationships between Two-Variables Review Sheet
Review Sheet 4. Which of the following is true? A) log(ab) = log A log B. D) log(a/b) = log A log B. B) log(a + B) = log A + log B. C) log A B = log A log B. 5. Suppose we measure a response variable Y
Section 2: Data & Measurement
Section 2: Data & Measurement Michael Gill Gov 50 September 21, 2011 Outline (0) Questions/Concerns (1) Review of Lecture (2) Measurement (3) Sampling Last Week Last week, we talked about theories, which
Lecture (chapter 1): Introduction
Lecture (chapter 1): Introduction Ernesto F. L. Amaral January 17, 2018 Advanced Methods of Social Research (SOCI 420) Source: Healey, Joseph F. 2015. Statistics: A Tool for Social Research. Stamford:
The Risk of Alcohol in Europe. Bridging the Gap June 2004
The Risk of Alcohol in Europe Bridging the Gap 16-19 June 2004 1. What is the relationship between alcohol and the risk of heart disease? 2. What is the relationship between alcohol and the risk of other
DEPARTMENT OF POLITICAL SCIENCE AND INTERNATIONAL RELATIONS Research Methods Posc 302 ANALYSIS OF SURVEY DATA
DEPARTMENT OF POLITICAL SCIENCE AND INTERNATIONAL RELATIONS Research Methods Posc 302 ANALYSIS OF SURVEY DATA I. TODAY S SESSION: A. Second steps in data analysis and interpretation 1. Examples and explanation
PARALLELISM AND THE LEGITIMACY GAP 1. Appendix A. Country Information
PARALLELISM AND THE LEGITIMACY GAP 1 Appendix A Country Information PARALLELISM AND THE LEGITIMACY GAP 2 Table A.1 Sample size by country 2006 2008 2010 Austria 2405 2255 0 Belgium 1798 1760 1704 Bulgaria
King, Keohane and Verba, Designing Social Inquiry, pp. 3-9, 36-46, notes for Comparative Politics Field Seminar, Fall, 1998.
King, Keohane and Verba, Designing Social Inquiry, pp. 3-9, 36-46, 115-149 notes for Comparative Politics Field Seminar, Fall, 1998. (pp. 1-9) King, Keohane and Verba s (from now on KKV) primary goal is
HER2 FISH pharmdx TM Interpretation Guide - Breast Cancer
P A T H O L O G Y HER2 FISH pharmdx TM Interpretation Guide - Breast Cancer For In Vitro Diagnostic Use FDA approved as an aid in the assessment of patients for whom Herceptin TM (trastuzumab) treatment
QPM Lab 9: Contingency Tables and Bivariate Displays in R
QPM Lab 9: Contingency Tables and Bivariate Displays in R Department of Political Science Washington University, St. Louis November 3-4, 2016 QPM Lab 9: Contingency Tables and Bivariate Displays in R 1
EFSA s Concise European food consumption database. Davide Arcella Data Collection and Exposure Unit
EFSA s Concise European food consumption database Davide Arcella Data Collection and Exposure Unit 1 The EFSA raison d être Risk assessment authority created in 2002 as part of a comprehensive program
Quantitative Analysis and Empirical Methods
2) Sciences Po, Paris, CEE / LIEPP University of Gothenburg, CERGU / Political Science 1 Explaining Relationships 2 Introduction We have learned about converting concepts into variables Our key interest
CARDIOVASCULAR DISEASE AND DIABETES:
CARDIOVASCULAR DISEASE AND DIABETES: HOW OECD HEALTH SYSTEMS DELIVER BETTER OUTCOMES? Progress report 7 th November 2013 Outline Overview of the project Preliminary results Descriptive Analytical Next
- Network for Excellence in Health Innovation
Real-world evidence is evidence from any and all sources of data that may contribute to more effective health care, including health care best tailored to the needs of individual patients. - Network for
Biology Report. Is there a relationship between Countries' Human Development Index (HDI) level and the incidence of tuberculosis?
Biology Report Is there a relationship between Countries' Human Development Index (HDI) level and the incidence of tuberculosis? Introduction Tuberculosis is a serious disease caused by the bacterium Mycobacterium
LEBANON. WCPT COUNTRY PROFILE December 2018
LEBANON WCPT COUNTRY PROFILE December 2018 LEBANON NUMBERS 1600 1400 1200 1000 800 600 400 200 0 Physical therapists in the country Members in MO 1,480 1,480 Total PTs in country 800000 700000 600000 500000
EUROPEAN AEROSOL PRODUCTION
Introduction EUROPEAN AEROSOL PRODUCTION 2017 EU European production Market share Production by segment Worldwide production / FEA, 2018. 1 INTRODUCTION More than 5.7 billion over 16 billion units globally
GENERAL PRINCIPLES ON FLEXIBILITY OF WORDING FOR HEALTH CLAIMS
GENERAL PRINCIPLES ON FLEXIBILITY OF WORDING FOR HEALTH CLAIMS These general principles were presented for the first time at an informal meeting in Brussels on 19 June 2012. Experts from 17 Member States
Arab American Voters 2014
Arab American Voters 2014 Their Identity and Political Concerns November 24, 2014 Executive Summary Identity and Personal Concerns Ethnic pride and identity remains high among Arab Americans. A majority
Manuel Cardoso RARHA Executive Coordinator Public Health MD Senior Advisor Deputy General-Director of SICAD - Portugal
Manuel Cardoso RARHA Executive Coordinator Public Health MD Senior Advisor Deputy General-Director of SICAD - Portugal Public Health Public health is the science and art of preventing disease, prolonging
Collecting Data: Observational Studies
STAT 250 Dr. Kari Lock Morgan Data Collection and Bias Collecting Data: Observational Studies SECTION 1.3 Association versus Causation Confounding s Observational Studies vs Experiments Population Sample
University of Oxford Intermediate Social Statistics: Lecture One
University of Oxford Intermediate Social Statistics: Lecture One Raymond M. Duch Nuffield College Oxford www.raymondduch.com @RayDuch January 17, 2012 Course Requirements Course Requirements Eight Lectures
Request for Letters of Intent. International Development of H5N1 Influenza Vaccines
Request for Letters of Intent International Development of H5N1 Influenza Vaccines The World Health Organization (WHO) intends to provide funding to developing (1) country vaccine manufacturers to develop
BioPlex 2200 Infectious Disease Panels
BioPlex 2200 System BioPlex 2200 Infectious Disease Panels An Expanding Multiplexed Assay Menu Lyme HIV Ag-Ab MMV IgM Syphilis Total & RPR MMRV EBV HSV-1 & HSV-2 EBV IgM ToRC IgM ToRC Leading the way with
Drinking guidelines used in the context of early identification and brief interventions in Europe: overview of RARHA survey results
Drinking guidelines used in the context of early identification and brief interventions in Europe: overview of RARHA survey results E. Scafato, C. Gandin, L. Galluzzo, S. Ghirini, S. Martire Istituto Superiore
Hearing Loss: The Statistics
: The Statistics 2015 Global Statistics It is hard to know precise numbers of how many people experience hearing loss across the EU, Europe, and indeed the world. There are many sources of information
DENMARK. WCPT COUNTRY PROFILE December 2018
DENMARK WCPT COUNTRY PROFILE December 2018 DENMARK NUMBERS 14000 12000 10000 8000 6000 4000 2000 0 Physical therapists in the country Members in MO 11,720 12,975 Total PTs in country 800000 700000 600000
Alcohol-related harm in Europe and the WHO policy response
Alcohol-related harm in Europe and the WHO policy response Lars Moller Programme Manager World Health Organization Regional Office for Europe Date of presentation NCD global monitoring framework: alcohol-related
Cross Border Genetic Testing for Rare Diseases
Cross Border Genetic Testing for Rare Diseases EUCERD Joint Action WP8 Helena Kääriäinen National Institute for Health an Welfare, Helsinki, Finland Starting point Possibilities and demand for genetic
Results you can trust
PRODUCT I NF OR MAT ION pharmdx Results you can trust The first and only FDA-approved PD-L1 test to assess the magnitude of treatment effect on progression-free survival in melanoma patients from OPDIVO
Yersiniosis SURVEILLANCE REPORT. Annual Epidemiological Report for Key facts. Methods. Epidemiology
SURVEILLANCE REPORT Annual Epidemiological Report for 2015 Yersiniosis Key facts In 2015, 26 countries reported 7 279 confirmed yersiniosis cases in the EU/EEA. The overall notification rate was 2.0 cases
Sponsor. Generic Drug Name. Trial Indication(s) Protocol Number. Protocol Title. Clinical Trial Phase. Study Start/End Dates. Novartis.
Sponsor Novartis Generic Drug Name Lumiracoxib Trial Indication(s) Not applicable Protocol Number CCOX189A2483 Protocol Title A retrospective pharmacogenetics analysis of patients with elevated liver enzymes
Thomas Karlsson & Esa Österberg National Research and Development Centre for Welfare and Health Alcohol and Drug Research Group P.O.
European Comparative Alcohol Study Europe and Alcohol Policy Thomas Karlsson & Esa Österberg National Research and Development Centre for Welfare and Health Alcohol and Drug Research Group P.O.BO 220 FIN-00531
Stat 13, Intro. to Statistical Methods for the Life and Health Sciences.
Stat 13, Intro. to Statistical Methods for the Life and Health Sciences. 0. SEs for percentages when testing and for CIs. 1. More about SEs and confidence intervals. 2. Clinton versus Obama and the Bradley
World Connections Committee (WCC) Report
World Connections Committee (WCC) Report 06 Co-Dependents Anonymous Service Conference Countries Where CoDA Exists This report reflects the World Connections Committee (WCC) support of the growth and development
GERMANY. WCPT COUNTRY PROFILE December 2018
GERMANY WCPT COUNTRY PROFILE December 2018 GERMANY NUMBERS 160000 140000 120000 100000 80000 60000 40000 20000 0 Physical therapists in the country Members in MO 21,502 136,000 Total PTs in country 800000
LAB 4 Experimental Design
LAB 4 Experimental Design Generally speaking, the research design that is used and the properties of the variables combine to determine what statistical tests we use to analyze the data and draw our conclusions.
Is there a relationship between Countries' Human Development Index (HDI) level and the incidence of tuberculosis?
Is there a relationship between Countries' Human Development Index (HDI) level and the incidence of tuberculosis? Introduction Tuberculosis is a serious disease caused by the bacterium Mycobacterium tuberculosis.
Overview of drug-induced deaths in Europe - What does the data tell us?
Overview of drug-induced deaths in Europe - What does the data tell us? João Matias, Isabelle Giraudon, Julián Vicente EMCDDA expert group on the key-indicator Drug-related deaths and mortality among drug
Chapter 11. Experimental Design: One-Way Independent Samples Design
11-1 Chapter 11. Experimental Design: One-Way Independent Samples Design Advantages and Limitations Comparing Two Groups Comparing t Test to ANOVA Independent Samples t Test Independent Samples ANOVA Comparing
PRODUCT INGREDIENT RELATED HEALTH CLAIMS ASSESSMENT for NU SKIN PHARMANEX PRODUCTS in EUROPE
PRODUCT INGREDIENT RELATED HEALTH CLAIMS ASSESSMENT for NU SKIN PHARMANEX PRODUCTS in EUROPE This document is established in support of the European distributors communication regarding the right use of
EPIDEMIOLOGY. Accurate, in-depth information for understanding and assessing targeted markets
EPIDEMIOLOGY Accurate, in-depth information for understanding and assessing targeted markets WHY ACCURATE MARKET FORECASTS Start with accurate understanding Epidemiology is the study of disease patterns
What s s on the Menu in Europe? - overview and challenges in the first pan- European food consumption survey
What s s on the Menu in Europe? - overview and challenges in the first pan- European food consumption survey Liisa Valsta Data Collection and Exposure Unit What s s on the menu in Europe? Background Attempts
Nutrient profiles for foods bearing claims
Nutrient profiles for foods bearing claims Fields marked with * are mandatory. Background Regulation (EC) 1924/2006 (Nutrition and Health Claims NHC Regulation) establishes EU rules on nutrition and health
MADE IN MOVEMBER GETTING IT GROWN FOR INQUIRIES PLEASE CONTACT FOR MORE INFORMATION ABOUT THE ORGANIZATION VISIT MADE IN MOVEMBER 2014 PRESS KIT 2
USA GETTING IT GROWN THE MOVEMBER FOUNDATION IS THE LEADING GLOBAL ORGANIZATION COMMITTED TO CHANGING THE FACE OF MEN S HEALTH. WE ACHIEVE THIS BY CHALLENGING MEN TO GROW MOUSTACHES DURING MOVEMBER (THE
The first and only fully-automated, multiplexed solution for Measles, Mumps, Rubella and Varicella-zoster virus antibody testing
Bio-Rad Laboratories BioPlex 2200 System BioPlex 2200 MMRV IgG Kit The first and only fully-automated, multiplexed solution for Measles, Mumps, Rubella and Varicella-zoster virus antibody testing Bio-Rad
Project Meeting Prague
Project Meeting Prague IO1 Assessment 9.11.217 CHRISTINA PADBERG ON BEHALF OF FRANKFURT UAS Current Status Assessment matrix was fully evaluated Experts have been interviewed, Interviews were fully evaluated
SS3 Series. Controlled Avalanche Power Diodes. Features: Mechanical Data: SMC/DO-214AB. Page 1 28/03/06 V1.0
SMC/DO-214AB Features: For surface mounted application. Metal to silicon rectifier, majority carrier conduction. Low forward voltage drop. Easy pick and place. High surge current capability. Epitaxial
ERNDIM QAP for qualitative urinary organic acid analysis. Annual Report 2003 (Sheffield)
Ms M Downing Dr J R Bonham Professor R J Pollitt Telephone (+)44 114 271 7404 Fax (+)44 114 276 6205 Department of Paediatric Chemical Pathology and Neonatal Screening The Children s Hospital Sheffield
Where we stand in EFORT
Where we stand in EFORT Engaging with the new EU regulatory landscape for medical devices. Challenges & opportunities Brussel, Belgium April 6, 2018 Per Kjaersgaard-Andersen Associate Professor Section