Andrea Rotnitzky presents "Efficient adjustment sets for population average causal effect estimation in graphical models"

Covariate adjustment is often used for estimation of population average causal effects (ATE). In recent years graphical rules have been derived for determining, from a causal diagram, all covariate adjustment sets. Restricting attention to causal linear models, a very recent article introduced two graphical criterions: one to compare the asymptotic variance of linear regression estimators that control for certain distinct adjustment sets and a second to identify the optimal adjustment set that provides the smallest asymptotic variance. In this talk, I will show that the same graphical criterions can be used in arbitrary causal diagrams when the goal is to minimize the asymptotic variance of non-parametric estimators of ATE that ignore the causal diagram assumptions. Furthermore, I will provide a graphical criterion to determine the optimal adjustment set among the minimal adjustment sets. In addition, I will provide another graphical criterion for determining when a non-parametric estimator of ATE is as efficient as an efficient estimator that exploits the causal diagram assumptions. Finally, I will show that for estimating the effect of time dependent treatments in the presence of time dependent confounders, there exist diagrams with no optimal adjustment sets.