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Causal inference and the potential outcomes model are now both commonly taught in graduate programs in epidemiology. However, I think we can all agree that counterfactual thinking can be a bit mind-bending at times and it is really easy to get lost deep in the weeds when trying to think through the potential for unobserved comparison groups or outcomes. In this episode of SERious Epi, we speak to Dr. Daniel Westreich about counterfactuals, the difference between causal inference and causal effect estimation, and assumptions required to estimate causal effects from observational data.

After listening to this podcast, if you’re interested in learning more about the potential outcomes model or checking out any of the resources mentioned on this podcast, links are included below:

– Rose, G. Sick individuals and sick populations. International Journal of Epidemiology 1985; 14:32–38.

– Greenland, S. For and Against Methodologies: Some Perspectives on Recent Causal and Statistical Inference Debates. Eur J Epidemiol 32, 3–20 (2017). https://doi.org/10.1007/s10654-017-0230-6

– Morabia, Alfredo. “On the Origin of Hill’s Causal Criteria.” Epidemiology 2, no. 5 (1991): 367-69. Accessed August 13, 2020. www.jstor.org/stable/20065702.

– Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.

-Westreich, D. (2020). Epidemiology by Design: A Causal Approach to the Health Sciences. https://global.oup.com/academic/product/epidemiology-by-design-9780190665760?cc=us&lang=en&

– Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15:615-625.

– Hernán MA, Taubman SL. Does obesity shorten life? The importance of well-defined interventions to answer causal questions. Int J Obes. 2008;32:s8-s14.

-Neyman, J (1923) http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.473.4367&rep=rep1&type=pdf

– Donald B Rubin (2005) Causal Inference Using Potential Outcomes, Journal of the American Statistical Association, 100:469, 322-331, DOI: 10.1198/016214504000001880

– Edwards JK, Cole SR, Westreich D. All your data are always missing: incorporating bias due to measurement error into the potential outcomes framework. Int J Epidemiol. 2015;44(4):1452-1459. doi:10.1093/ije/dyu272

– Westreich D, Edwards JK, Cole SR, Platt RW, Mumford SL, Schisterman EF. Imputation approaches for potential outcomes in causal inference. Int J Epidemiol. 2015;44(5):1731-1737. doi:10.1093/ije/dyv135

Podcast: Play in new window | Download (Duration: 56:30 — 77.6MB)

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At SER 2019, the Cassel lecture was delivered by Miguel Hernán and Sandro Galea on the topic of  reconciling social epidemiology and causal inference. Their talk was turned into a paper in the American Journal of Epidemiology, and in March 2020, was published along with a series of responses by Drs. Enrique Schisterman, Whitney Robinson and Zinzi Bailey, Tyler VanderWeele, and John Jackson and Onyebuchi Arah.  In this SERious Epi bonus journal club episode, we had conversation with Dr. John Jackson and Dr. Onyebuchi Arah about their commentary and had the opportunity to ask their thoughts on the other topics published in that issue.

Links:

• Win-Win: Reconciling Social Epidemiology and Causal Inference by Sandro Galea and Miguel A Hernán
  https://doi.org/10.1093/aje/kwz158
• Editorial: Let’s Be Causally Social by Enrique F Schisterman
  https://doi.org/10.1093/aje/kwz237
• Invited Commentary: What Social Epidemiology Brings to the Table—Reconciling Social Epidemiology and Causal Inference by Whitney R Robinson, Zinzi D Bailey
  https://doi.org/10.1093/aje/kwz197
• Invited Commentary: Counterfactuals in Social Epidemiology—Thinking Outside of “the Box”
  https://doi.org/10.1093/aje/kwz198
• Invited Commentary: Making Causal Inference More Social and (Social) Epidemiology More Causal by John W Jackson and Onyebuchi A Arah
  https://doi.org/10.1093/aje/kwz199
• Galea and Hernán Respond to “Brings to the Table,” “Differential Measurement Error,” and “Causal Inference in Social Epidemiology”
  https://doi.org/10.1093/aje/kwz201