Testing for causal effect for binary data when propensity scores are estimated through Bayesian Networks
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.
Cite
@article{arxiv.2302.07663,
title = {Testing for causal effect for binary data when propensity scores are estimated through Bayesian Networks},
author = {Paola Vicard and Paola Maria Vittoria Rancoita and Federica Cugnata and Alberto Briganti and Fulvia Mecatti and Clelia Di Serio and Pier Luigi Conti},
journal= {arXiv preprint arXiv:2302.07663},
year = {2024}
}