Confounder Selection via Support Intersection

TL;DR

This paper introduces support intersection-based variable selection methods to identify confounders in observational studies, addressing the large p small n challenge and improving causal effect estimation.

Contribution

It proposes novel confounder selection techniques based on support intersection under sparsity assumptions, enhancing causal inference accuracy.

Findings

Support intersection methods outperform heuristic approaches in simulations.

The methods effectively identify confounders in real datasets.

Improved causal effect estimation demonstrated through numerical experiments.

Abstract

Confounding matters in almost all observational studies that focus on causality. In order to eliminate bias caused by connfounders, oftentimes a substantial number of features need to be collected in the analysis. In this case, large p small n problem can arise and dimensional reduction technique is required. However, the traditional variable selection methods which focus on prediction are problematic in this setting. Throughout this paper, we analyze this issue in detail and assume the sparsity of confounders which is different from the previous works. Under this assumption we propose several variable selection methods based on support intersection to pick out the confounders. Also we discussed the different approaches for estimation of causal effect and unconfoundedness test. To aid in our description, finally we provide numerical simulations to support our claims and compare to common heuristic methods, as well as applications on real dataset.