A unified framework for efficient estimation of general treatment models

This paper presents a weighted optimization framework that unifies the binary, multivalued, and continuous treatment - as well as mixture of discrete and continuous treatment - under a unconfounded treatment assignment. With a general loss function, the framework includes the average, quantile, and...

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Veröffentlicht in:Quantitative economics 2021-07, Vol.12 (3), p.779-816
Hauptverfasser: Ai, Chunrong, Linton, Oliver, Motegi, Kaiji, Zhang, Zheng
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container_title Quantitative economics
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creator Ai, Chunrong
Linton, Oliver
Motegi, Kaiji
Zhang, Zheng
description This paper presents a weighted optimization framework that unifies the binary, multivalued, and continuous treatment - as well as mixture of discrete and continuous treatment - under a unconfounded treatment assignment. With a general loss function, the framework includes the average, quantile, and asymmetric least squares causal effect of treatment as special cases. For this general framework, we first derive the semiparametric efficiency bound for the causal effect of treatment, extending the existing bound results to a wider class of models. We then propose a generalized optimization estimator for the causal effect with weights estimated by solving an expanding set of equations. Under some sufficient conditions, we establish the consistency and asymptotic normality of the proposed estimator of the causal effect and show that the estimator attains the semiparametric efficiency bound, thereby extending the existing literature on efficient estimation of causal effect to a wider class of applications. Finally, we discuss estimation of some causal effect functionals such as the treatment effect curve and the average outcome. To evaluate the finite sample performance of the proposed procedure, we conduct a small-scale simulation study and find that the proposed estimation has practical value. In an empirical application, we detect a significant causal effect of political advertisements on campaign contributions in the binary treatment model, but not in the continuous treatment model.
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subjects Advertisements
Advertising campaigns
Analysis
C14
C21
Campaign contributions
Campaigns
Causal effect
Econometrics
Efficiency
entropy maximization
Normality
Optimization
Probability distribution
semiparametric efficiency
sieve method
Simulation
stabilized weights
treatment effect
Treatment methods
title A unified framework for efficient estimation of general treatment models
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