Casual Methods in the Service of Good Epidemiological Practice: A Roadmap

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Epidemiological Practice: A Roadmap Maya Petersen, University of

1 University of California, Berkeley From the SelectedWorks of Maya Petersen 2013 Casual Methods in the Service of Good Epidemiological Practice: A Roadmap Maya Petersen, University of California, Berkeley Available at:

of Biosta3s3cs and Epidemiology School of Public Health

2 Causal Methods in the Service of Good Epidemiological Prac:ce: A Road Map Maya Petersen Divs. of Biosta3s3cs and Epidemiology School of Public Health University of California, Berkeley Society for Epidemiological Research, Boston, MA, June 21, 2013

3 We must ask causal ques:ons Not all ques:ons are causal, but many are How things work How best to intervene to improve health Answering causal ques:ons is HARD Formal causal frameworks to the rescue DAGs! Counterfactuals! Marginal Structural Models! Maya as Grad Student

4 Here Be Dragons. Overconfidence in assump:ons I can draw a causal graph - > My graph is useful! My graph represents reality! Misinterpreta:on of results My observa:onal analysis now replica:ng a trial - > Why does my trial not match my analysis results? Complexity obscuring common sense - >???

5 Formal Causal Frameworks are a Tool What are they useful for? 1. Make uncertainty explicit 2. Frame bewer ques:ons 3. Understand assump:ons 4. Improve study design 5. Design sta:s:cal analyses that come closer to answering our ques:ons 6. Interpret results appropriately

6 How? A roadmap 1. Specify Causal Model represen:ng real background knowledge 2. Specify Observed Data and link to causal model 3. Specify Causal Ques2on 4. Iden2fy - Knowledge + data sufficient? 5. Commit to an es2mand as close to ques:on as possible, and a sta:s:cal model represen:ng real knowledge. 6. Es2mate. 7. Interpret Results.

7 1. Specify Causal Model Good Epidemiologic Prac:ce: Use background knowledge Confounding, selec:on bias, measurement error. Causal Framework: A formal language for expressing it Makes knowledge more powerful Moving beyond intui:on We take for granted that intui:on is not enough to help us make sense of complex data- that is why we have sta:s:cs Same for causal reasoning. Intui:on breaks down

8 Causal Models Express Knowledge Example: Causal Graphs/Structural Causal Model CD4 Count An:retroviral Therapy Mortality Many formal causal frameworks: Counterfactual/Poten:al Outcome, Non- Parametric Structural Equa:on Models, Single World Interven:on Graphs, FFRCISTG, Decision Theore:c.

9 Causal Models Express Uncertainty We have real knowledge, but it is limited Which variables affect each other? Unmeasured factors? Func:onal form of those rela:onships? U Homelessness, Substance use CD4 Count An:retroviral Therapy Mortality

All causal models are not wrong Causal models should represent real knowledge We oben need to make addi:onal assump:ons in order to make progress Keep this process

10 All causal models are not wrong Causal models should represent real knowledge We oben need to make addi:onal assump:ons in order to make progress Keep this process separate Beware the tempta:on to oversimplify U Homelessness, Substance use CD4 Count Homelessness, Substance use CD4 Count An:retroviral Therapy An:retroviral Therapy Mortality

11 All causal models are not wrong Causal models should represent real knowledge We oben need to make addi:onal assump:ons in order to make progress Keep this process separate Beware the tempta:on to oversimplify Use a simpler model? Not unless your knowledge jus:fies it Can use a different language to express your knowledge and lack thereof

12 2. Specify Observed Data and its link to the Causal Model Causal Model: What we know Observed Data: What we Measure Causal model describes the set of processes that may have given rise to the observed data Implies the set of distribu:ons possible for the observed data: The Sta:s:cal Model

13 All sta:s:cal models are not wrong Sta:s:cal Models should represent real knowledge Good Epidemiologic Prac:ce: Choose an appropriate sta:s:cal model Causal Framework: Can help us choose sta:s:cal models that reflect our uncertainty Oben put no restric:ons on the joint distribu:on of our observed variables: Non- parametric Some:mes we do have real knowledge By all means use it!

14 A Roadmap. 1. Causal Model Represen:ng background knowledge and uncertainty 2. Observed Data Process that generated the data described by the causal model Sta2s2cal Model Possible distribu:ons for the Observed data

15 3. Specify Causal Ques2on Good Epidemiological Prac:ce: State your scien:fic ques:on clearly Causal Framework: A language to translate your ques:on into a formal query Ex: Using counterfactuals Forces you to be explicit about exactly which experiment (change(s) to the system of interest) would let you answer your ques:on

16 Counterfactual Interven:ons Example: Simple sta:c interven:ons CD4 Count Yes An:retroviral Therapy No CD4 Count Counterfactual Mortality with ART An:retroviral Therapy Counterfactual Mortality without ART

17 Very flexible- Make the target quan:ty match the ques:on Example: Dynamic Regimes If CD4< 350 CD4 Count An:retroviral Therapy Counterfactual Mortality If CD4<500 CD4 Count An:retroviral Therapy Counterfactual Mortality

18 Many more op:ons! Longitudinal Effects Effect of An:retroviral Therapy (ART) over :me Direct Effects Effect of ART not mediated by CD4 changes Missing Data and Censoring Interven:ons to prevent losses to follow up Stochas:c Interven:ons Shib the delay from diagnosis to ART start Can also summarize the distribu:on of the counterfactual outcomes in lots of ways

19 4. Iden2fy. Knowledge + Data Sufficient? Our ques:on is stated of in terms of counterfactual quan::es What would things have looked like under different condi:ons Can we translate our target causal quan:ty into a sta:s:cal parameter? A func:on of the observed data distribu:on, or es2mand If so, which es:mand?

20 Intui:on only gets you so far. Good Epidemiological Prac:ce: Control for confounding Causal Framework Which variables to control for Adjus:ng for some pre- treatment variables is harmful! Even when no single adjustment set is sufficient, your target parameter may s:ll be iden:fied Longitudinal effects and :me dependent confounding Effect media:on and direct effects Instrumental variables Many more.. Many of these es2mands are not intui2ve

21 1. Causal Model 2. Data A Roadmap. Sta2s2cal Model 4. Iden2fied? Y Are data sufficient to answer causal ques:on under model assump:ons 3. Ques2on Translate the scien:fic ques:on into a formal causal quan:ty (using counterfactuals) 5. Es2mand Equal to the target causal quan:ty

22 In many (most?) cases, data + model are NOT sufficient Good Epidemiological Prac:ce Get more data Do the best job you can with data you have, and understand limita:ons Formal Causal Framework: Which data/how to change design What addi:onal assump:ons are needed: convenience assump2ons? Es:mand that comes closest to answering our ques:on with the data we have

23 1. Causal Model 2. Data A Roadmap. Sta2s2cal Model Y 4. Iden2fied? 5. Es2mand Equal or as close as possible to the target causal quan:ty 3. Ques2on N Convenience assump2ons

24 6. Es2mate Causal framework got us here but this step is purely sta:s:cal One es:mator is no more causal than another IPW not more causal than logis:c regression Es:mators have different sta:s:cal proper:es Bias, variance, robustness, consistency. Given a sta:s:cal model and es:mand, we can study these proper:es and pick the best for our problem

25 Causal inference can be hard, but so can sta:s:cs Good Epidemiological Prac:ce: The ques:on should drive the sta:s:cal analysis Causal Framework : Knowledge + data + ques:on oben = hard sta:s:cal problems Many es:mands do not correspond to a coefficient in a single regression Complexity is not an end in itself Complex sta:s:cal methods are some:mes necessary to get the best possible answer to real world ques:ons

26 A Roadmap. 1. Causal Model 2. Data 3. Ques2on Sta2s2cal Model Y 4. Iden2fied? 5. Es2mand N Convenience assump2ons 6. Es2mator

27 7. Interpret Results Sta:s:cal Interpreta:on? With correct sta:s:cal model and good es:mator Causal Interpreta:on? If causal model + convenience assump:ons are true Makes explicit what these are Impact of a real world interven:on? Many more assump:ons Stability of condi:ons and popula:on Interference Correspondence between hypothe:cal and real world interven:on

28 A Roadmap. 1. Causal Model 2. Data 3. Ques2on Sta2s2cal Model Y 4. Iden2fied? 5. Es2mand N Convenience assump2ons 6. Es2mator 7. Interpreta2on

As we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know.

29 As we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns - - the ones we don't know we don't know. Causal frameworks used well Keep us uncomfortably aware of how liwle we know While not freezing us into panicked inac:on A systema:c approach to using them can help epidemiology improve public health

30 Acknowledgments Mark van der Laan References too numerous to list Based on the work and insights of many Judea Pearl James Robins Miguel Hernan.many others My work supported by The Doris Duke Charitable Founda:on Clinical Scien:st Development Award

Applica(on of Causal Inference Methods to Improve Treatment of HIV in Resource Limited Se?ngs

Applica(on of Causal Inference Methods to Improve Treatment of HIV in Resource Limited Se?ngs Maya Petersen works.bepress.com/maya_petersen Divisions of Biosta,s,cs and Epidemiology, University of California,