Partial identification and inference for conditional distributions of treatment effects

Summary This paper considers identification and inference for the distribution of treatment effects conditional on observable covariates. Since the conditional distribution of treatment effects is not point identified without strong assumptions, we obtain bounds on the conditional distribution of tr...

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Published in:Journal of applied econometrics (Chichester, England) England), 2024-01, Vol.39 (1), p.107-127
Main Author: Lee, Sungwon
Format: Article
Language:English
Subjects:
conditional distribution
Dominance
Econometrics
Endogenous
heterogeneity
Inference
partial identification
treatment effects
uniform inference
Usefulness
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description Summary This paper considers identification and inference for the distribution of treatment effects conditional on observable covariates. Since the conditional distribution of treatment effects is not point identified without strong assumptions, we obtain bounds on the conditional distribution of treatment effects by using the Makarov bounds. We also consider the case where the treatment is endogenous and propose two stochastic dominance assumptions to tighten the bounds. We develop a nonparametric framework to estimate the bounds and establish the asymptotic theory that is uniformly valid over the support of treatment effects. An empirical example illustrates the usefulness of the methods.
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source International Bibliography of the Social Sciences (IBSS); Wiley-Blackwell Read & Publish Collection
subjects conditional distribution
Dominance
Econometrics
Endogenous
heterogeneity
Inference
partial identification
treatment effects
uniform inference
Usefulness
title Partial identification and inference for conditional distributions of treatment effects
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