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Distributional Treatment Effect with Finite Mixture

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  • Myungkou Shin

Abstract

Treatment effect heterogeneity is of a great concern when evaluating the treatment. However, even with a simple case of a binary treatment, the distribution of treatment effect is difficult to identify due to the fundamental limitation that we cannot observe both treated potential outcome and untreated potential outcome for a given individual. This paper assumes a finite mixture model on the potential outcomes and a vector of control covariates to address treatment endogeneity and imposes a Markov condition on the potential outcomes and covariates within each type to identify the treatment effect distribution. The mixture weights of the finite mixture model are consistently estimated with a nonnegative matrix factorization algorithm, thus allowing us to consistently estimate the component distribution parameters, including ones for the treatment effect distribution.

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  • Myungkou Shin, 2024. "Distributional Treatment Effect with Finite Mixture," Papers 2403.18503, arXiv.org.
    • Handle: RePEc:arx:papers:2403.18503
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    References listed on IDEAS

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    1. Tetsuya Kaji & Jianfei Cao, 2023. "Assessing Heterogeneity of Treatment Effects," Papers 2306.15048, arXiv.org.
    2. Brantly Callaway & Tong Li, 2019. "Quantile treatment effects in difference in differences models with panel data," Quantitative Economics, Econometric Society, vol. 10(4), pages 1579-1618, November.
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