Standard regression adjustment gives inconsistent estimates of causal effects when there are time-varying treatment effects and time-varying covariates. Loosely speaking, the issue is that some covariates are post-treatment variables because they may be affected by prior treatment status, and regressing out post-treatment variables causes bias. Motivated by this, we ask: How can we modify regression methods so that they give valid causal estimates in this setting? We develop an estimator for this setting based on regression modeling (linear, log-linear, probit and Cox regression), proving that it is consistent for the causal effect of interest.
Speaker: Stephen Bates, PhD, Postdoctoral Researcher in Statistics and Computer Science, UC Berkeley