Possibility of using overrule to evaluate overlap in causal inference

Abstract

Causal inference is a widely recognized concept in various domains, including medicine, for estimating the effect of a medication on a certain disease. During this estimation, overlap is commonly used to eliminate the error caused by other features. However, finding the real overlap region in practice is challenging due to the limited sample size and unknown data distribution. Therefore, some machine-learning methods have been proposed to estimate the overlap region. One such method is Overrule, a Python package proposed by Oberst et al. Overrule is based on rule-based lassification and estimates the overlap region by interpreting it as several rules across the features. However, it is still unclear how Overrule performs under different circumstances. Thus, the primary bjective of this project is to test the performance of Overrule with different datasets. To accomplish this, a series of tests are built and executed to evaluate the performance of Overrule in diverse scenarios.