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.

Cugnata, F., Rancoita, P.M.V., Luigi Conti, P., Briganti, A., Di Serio, C., Mecatti, F., et al. (2021). A propensity score approach for treatment evaluation based on Bayesian Networks. In 50th SIS Scientific Meeting of the Italian Statistical Society, Book of short papers-SIS 2021 (pp.1524-1529). Pearson.

A propensity score approach for treatment evaluation based on Bayesian Networks

Paola Vicard
2021-01-01

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.
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Cugnata, F., Rancoita, P.M.V., Luigi Conti, P., Briganti, A., Di Serio, C., Mecatti, F., et al. (2021). A propensity score approach for treatment evaluation based on Bayesian Networks. In 50th SIS Scientific Meeting of the Italian Statistical Society, Book of short papers-SIS 2021 (pp.1524-1529). Pearson.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/395494
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