Testing for causal effect for binary data when propensity scores are estimated through Bayesian Networks

TL;DR

This paper introduces a Bayesian Network-based method for estimating causal effects in observational studies with binary outcomes, providing a flexible and efficient alternative to traditional propensity score approaches.

Contribution

It develops a novel approach using Bayesian Networks to estimate propensity scores, enabling more accurate causal inference with binary data and discrete covariates.

Findings

Bayesian Network-based propensity scores outperform traditional methods in simulations.

The proposed estimators have desirable asymptotic properties for hypothesis testing.

Empirical results demonstrate the method's effectiveness on real-world prostate cancer data.

Abstract

This paper proposes a new statistical approach for assessing treatment effect using Bayesian Networks (BNs). The goal is to draw causal inferences from observational data with a binary outcome and discrete covariates. The BNs are here used to estimate the propensity score, which enables flexible modeling and ensures maximum likelihood properties, including asymptotic efficiency. %As a result, other available approaches cannot perform better. When the propensity score is estimated by BNs, two point estimators are considered - H\'ajek and Horvitz-Thompson - based on inverse probability weighting, and their main distributional properties are derived for constructing confidence intervals and testing hypotheses about the absence of the treatment effect. Empirical evidence is presented to show the goodness of the proposed methodology on a simulation study mimicking the characteristics of a real dataset of prostate cancer patients from Milan San Raffaele Hospital.