In observational studies evaluating the treatment effect on a given outcome, the treated and untreated subjects may be highly unbalanced in their observed covariates, and these differences can lead to biased estimates of treatment effects. Propensity score is popular tool to reduce this bias. In this work we propose to estimate the propensity score by using Bayesian Networks as alternative to conventional logistic regression. Based on it, we develop an inferential methodology to evaluate the treatment effect. In simulation study, our proposed approach resulted in the best performance.

A propensity score approach for treatment evaluation based on Bayesian Networks / Cugnata, Federica; Rancoita, Paola; Conti, Pier Luigi; Briganti, Alberto; Di Serio, Clelia; Mecatti, Fulvia; Vicard, Paola. - (2021), pp. 660-665.

A propensity score approach for treatment evaluation based on Bayesian Networks

Conti Pier Luigi
Methodology
;
Di Serio Clelia;Mecatti Fulvia;Vicard Paola
2021

Abstract

In observational studies evaluating the treatment effect on a given outcome, the treated and untreated subjects may be highly unbalanced in their observed covariates, and these differences can lead to biased estimates of treatment effects. Propensity score is popular tool to reduce this bias. In this work we propose to estimate the propensity score by using Bayesian Networks as alternative to conventional logistic regression. Based on it, we develop an inferential methodology to evaluate the treatment effect. In simulation study, our proposed approach resulted in the best performance.
2021
Book of short papers - SIS 2021
9788891927361
potential outcomes; propensity score; covariate balance; observational study ; ATE estimation
02 Pubblicazione su volume::02a Capitolo o Articolo
A propensity score approach for treatment evaluation based on Bayesian Networks / Cugnata, Federica; Rancoita, Paola; Conti, Pier Luigi; Briganti, Alberto; Di Serio, Clelia; Mecatti, Fulvia; Vicard, Paola. - (2021), pp. 660-665.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1664974
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