Confounding and Collapsibility in Causal Inference

By Sander Greenland, James M. Robins, Judea Pearl. 1999

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Citations in the corpus

Doubly robust estimation of causal effects. (2011, background): “If the distributions of confounders are the same within each exposure group, then there is no longer an association between the confounders and exposure, making the exposed and unexposed exchangeable (15).”
The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. (2013, background): “represent changes in the log odds of the outcome) and we assume the outcome is uncommon at all covariate levels to avoid complications arising from noncollapsibility of odds ratios (9, 10).”
Mediation Analysis: A Practitioner's Guide. (2016, background): “…the correct causal structure ov i it y A cce B = γλ γ + λ − . can alter the direct and indirect effects) we can take our direct and indirect effects estimates and their conﬁdence intervals from the observed data and divide the direct effect estimate and both limits of its conﬁdence interval by…”
Mendelian randomisation for mediation analysis: current methods and challenges for implementation (2019, background): “This means the association between an exposure and outcome would not be constant on the odds-ratio scale by strata of categorical covariate [34, 35].”
The role of prediction modeling in propensity score estimation: an evaluation of logistic regression, bCART, and the covariate-balancing propensity score. (2014, background): “Although binary outcomes are more common in pharmacoepidemiology, we chose to simulate a continuous outcome to simplify the analysis and to avoid issues with the noncollapsibility of the odds ratio (22).”