In observational studies evaluating the treatment effect on a given out- come, 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 esti- mate 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., i Conti, P., Briganti, A., Di Serio, C., Mecatti, F., et al. (2021). A propensity score approach for treatment evaluation based on Bayesian Networks. In Book of Short Paper SIS2021 (pp.1524-1529). Milano : Pearson.

A propensity score approach for treatment evaluation based on Bayesian Networks

Mecatti, F;
2021

Abstract

In observational studies evaluating the treatment effect on a given out- come, 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 esti- mate 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.
No
paper
Potential outcomes, propensity score, covariate balance, observational study, ATE estimation
English
Riunione Scientifica SIS2021
9788891927361
https://it.pearson.com//docenti/universita/partnership/sis.html
Cugnata, F., Rancoita, P., i Conti, P., Briganti, A., Di Serio, C., Mecatti, F., et al. (2021). A propensity score approach for treatment evaluation based on Bayesian Networks. In Book of Short Paper SIS2021 (pp.1524-1529). Milano : Pearson.
Cugnata, F; Rancoita, P; i Conti, P; Briganti, A; Di Serio, C; Mecatti, F; Vicard, P
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/366602
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