A0944
Title: An exhaustive selection of sufficient adjustment sets for causal inference
Authors: Wei Luo - Zhejiang University (China) [presenting]
Fei Qin - Hong Kong Baptist University (China)
Lixing Zhu - Beijing Normal University (China)
Abstract: A subvector of predictor that satisfies the ignorability assumption, whose index set is called a sufficient adjustment set, is crucial for conducting reliable causal inference based on observational data. A general family of methods is proposed to detect all such sets for the first time in the literature, with no parametric assumptions on the outcome models and with flexible parametric and semiparametric assumptions on the predictor within the treatment groups; the latter induces desired sample-level accuracy. It is shown that the collection of sufficient adjustment sets can uniquely facilitate multiple types of studies in causal inference, including sharpening the estimation of average causal effect and recovering fundamental connections between the outcome and the treatment hidden in the dependence structure of the predictor. These findings are illustrated by simulation studies and a real data example at the end.
