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

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  • Stephen G. Donald
  • Yu-Chin Hsu
  • Robert P. Lieli

Abstract

We propose inverse probability weighted estimators for 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 efficient. When the (binary) instrument satisfies one-sided noncompliance, 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 noncompliance 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 pretesting. We illustrate the implementation of the test on data from training programs administered under the Job Training Partnership Act in the United States. This article has online supplementary material.

Suggested Citation

  • Stephen G. Donald & Yu-Chin Hsu & Robert P. Lieli, 2014. "Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 395-415, July.
    • Handle: RePEc:taf:jnlbes:v:32:y:2014:i:3:p:395-415
    DOI: 10.1080/07350015.2014.888290
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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