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Distributional Treatment Effect with Latent Rank Invariance

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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 random 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 conditional independence assumption that the two potential outcomes are independent of each other given a scalar latent variable. Using two proxy variables, we identify conditional distribution of the potential outcomes given the latent variable. To pin down the location of the latent variable, we assume strict monotonicty on some functional of the conditional distribution; with specific example of strictly increasing conditional expectation, we label the latent variable as 'latent rank' and motivate the identifying assumption as 'latent rank invariance.'

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  • Myungkou Shin, 2024. "Distributional Treatment Effect with Latent Rank Invariance," Papers 2403.18503, arXiv.org, revised Jun 2024.
    • 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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