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Main Authors: Lee, Ying-Ying, Liu, Chu-An
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2411.04312
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author Lee, Ying-Ying
Liu, Chu-An
author_facet Lee, Ying-Ying
Liu, Chu-An
contents We study causal inference in sample selection models where a continuous or multivalued treatment affects both outcome and their observability (eg., employment or survey response). We generalized the widely used Lee (2009)'s bounds for binary treatment effects. Our key innovation is a sufficient treatment value assumption that imposes weak restrictions on selection heterogeneity and is implicit in separable threshold-crossing models, including monotone effects on selection. Our double debiased machine learning estimator enables nonparametric and high-dimensional methods, using covariates to tighten the bounds and capture heterogeneity. Applications to Job Corps and Civilian Conservation Corps (CCC) program evaluations reinforce prior findings under weaker assumptions.
format Preprint
id arxiv_https___arxiv_org_abs_2411_04312
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Lee Bounds with a Continuous Treatment in Sample Selection
Lee, Ying-Ying
Liu, Chu-An
Econometrics
We study causal inference in sample selection models where a continuous or multivalued treatment affects both outcome and their observability (eg., employment or survey response). We generalized the widely used Lee (2009)'s bounds for binary treatment effects. Our key innovation is a sufficient treatment value assumption that imposes weak restrictions on selection heterogeneity and is implicit in separable threshold-crossing models, including monotone effects on selection. Our double debiased machine learning estimator enables nonparametric and high-dimensional methods, using covariates to tighten the bounds and capture heterogeneity. Applications to Job Corps and Civilian Conservation Corps (CCC) program evaluations reinforce prior findings under weaker assumptions.
title Lee Bounds with a Continuous Treatment in Sample Selection
topic Econometrics
url https://arxiv.org/abs/2411.04312