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

Paola Vicard (), Paola Maria Vittoria Rancoita (), Federica Cugnata (), Alberto Briganti (), Fulvia Mecatti (), Clelia Serio () and Pier Luigi Conti ()
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Paola Vicard: Roma Tre University
Paola Maria Vittoria Rancoita: Vita-Salute San Raffaele University
Federica Cugnata: Vita-Salute San Raffaele University
Alberto Briganti: Vita-Salute San Raffaele University
Fulvia Mecatti: University of Milano-Bicocca
Clelia Serio: Vita-Salute San Raffaele University
Pier Luigi Conti: Sapienza University of Rome

AStA Advances in Statistical Analysis, 2025, vol. 109, issue 3, No 5, 483-508

Abstract: 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. When the propensity score is estimated by BNs, two point estimators are considered—Hájek 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 good behavior of the proposed methodology through a simulation study mimicking the characteristics of a real dataset of prostate cancer patients from Milan San Raffaele Hospital.

Keywords: Bayesian Network; Propensity score; Covariate balance; Observational study; ATE estimation; Testing treatment effect (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10182-025-00535-4

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