Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT

Stephen G. Donald (), Yu-Chin Hsu and Robert Lieli
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Stephen G. Donald: Department of Economics, University of Texas at Austin

No 12-A017, IEAS Working Paper : academic research from Institute of Economics, Academia Sinica, Taipei, Taiwan

Abstract: We propose inverse probability weighted estimators for the the local average treatment effect (LATE) and the local average treatment effect for the treated (LATT) under instrumental variable assumptions with covariates. We show that these estimators are asymptotically normal and effcient. When the (binary) instrument satisfies one-sided non-compliance, we propose a Durbin- Wu-Hausman-type test of whether treatment assignment is unconfounded conditional on some observables. The test is based on the fact that under one-sided non-compliance LATT coincides with the average treatment effect for the treated (ATT). We conduct Monte Carlo simulations to demonstrate, among other things, that part of the theoretical efficiency gain afforded by unconfoundedness in estimating ATT survives pre-testing. We illustrate the practical implementation of the test on data from training programs administered under the Job Training Partnership Act.

Keywords: local average treatment effect; instrumental variables; unconfoundedness; inverse probability weighted estimation; nonparametric estimation (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2012-12
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (6)

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Journal Article: Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:sin:wpaper:12-a017

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