Estimation of conditional average treatment effects with high-dimensional data

Estimation of conditional average treatment effects with high-dimensional data

Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of...

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Bibliographic Details
Main Authors: FAN, Qingliang, HSU, Yu-Chin, LIELI, Robert P., ZHANG, Yichong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2020
Subjects:
Heterogenous treatment effects
high-dimensional data
uniform confidence band
Econometrics
Online Access:https://ink.library.smu.edu.sg/soe_research/2455
https://ink.library.smu.edu.sg/context/soe_research/article/3454/viewcontent/Unconditional_Quantile_Regression_High_D_sv.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/soe_research/2455
https://ink.library.smu.edu.sg/context/soe_research/article/3454/viewcontent/Unconditional_Quantile_Regression_High_D_sv.pdf

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