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 C. Perna, N. Salvati, F. Schirripa Spagnolo (a cura di), 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.
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