An exhaustive selection of sufficient adjustment sets for causal inference

TL;DR

This paper introduces a comprehensive method to identify all sufficient adjustment sets for causal inference without relying on parametric outcome models, enhancing the accuracy and reliability of observational data analysis.

Contribution

It proposes a novel, general family of methods to detect all sufficient adjustment sets under minimal assumptions, advancing causal inference techniques.

Findings

Method successfully identifies all sufficient adjustment sets.

Improves estimation accuracy of causal effects.

Demonstrated effectiveness through simulations and real data.

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. In this paper, we propose a general family of methods 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. We show 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.