Bayesian nonparametric mixtures of categorical directed graphs for heterogeneous causal inference

TL;DR

This paper introduces a Bayesian nonparametric approach using mixtures of categorical DAGs to infer personalized causal effects in heterogeneous populations, demonstrated through simulations and a breast cancer case study.

Contribution

It develops a novel Bayesian methodology combining Dirichlet Process mixtures with categorical DAGs for personalized causal inference in heterogeneous groups.

Findings

Effective clustering of individuals by causal structure.

Accurate estimation of subject-specific causal effects.

Successful application to breast cancer therapy data.

Abstract

Quantifying causal effects of exposures on outcomes, such as a treatment and a disease respectively, is a crucial issue in medical science for the administration of effective therapies. Importantly, any related causal analysis should account for all those variables, e.g. clinical features, that can act as risk factors involved in the occurrence of a disease. In addition, the selection of targeted strategies for therapy administration requires to quantify such treatment effects at personalized level rather than at population level. We address these issues by proposing a methodology based on categorical Directed Acyclic Graphs (DAGs) which provide an effective tool to infer causal relationships and causal effects between variables. In addition, we account for population heterogeneity by considering a Dirichlet Process mixture of categorical DAGs, which clusters individuals into homogeneous groups characterized by common causal structures, dependence parameters and causal effects. We develop computational strategies for Bayesian posterior inference, from which a battery of causal effects at subject-specific level is recovered. Our methodology is evaluated through simulations and applied to a dataset of breast cancer patients to investigate cardiotoxic side effects that can be induced by the administrated anticancer therapies.