A Causal Direction Test for Heterogeneous Populations

TL;DR

This paper introduces an adjusted causal direction test for heterogeneous populations, improving causal inference accuracy when data come from mixed groups, validated through simulations and real data applications.

Contribution

It proposes a novel adjustment to causal direction testing that accounts for heterogeneity using a data-driven clustering approach, enhancing reliability in diverse populations.

Findings

Adjusted test improves accuracy in heterogeneous data

Simulation results show significant performance gains

Real data application demonstrates practical utility

Abstract

A probabilistic expert system emulates the decision-making ability of a human expert through a directional graphical model. The first step in building such systems is to understand data generation mechanism. To this end, one may try to decompose a multivariate distribution into product of several conditionals, and evolving a blackbox machine learning predictive models towards transparent cause-and-effect discovery. Most causal models assume a single homogeneous population, an assumption that may fail to hold in many applications. We show that when the homogeneity assumption is violated, causal models developed based on such assumption can fail to identify the correct causal direction. We propose an adjustment to a commonly used causal direction test statistic by using a $k$-means type clustering algorithm where both the labels and the number of components are estimated from the collected data to adjust the test statistic. Our simulation result show that the proposed adjustment significantly improves the performance of the causal direction test statistic for heterogeneous data. We study large sample behaviour of our proposed test statistic and demonstrate the application of the proposed method using real data.