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Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental Variable

  • Peter M. Aronow (a1) and Allison Carnegie (a2)
Abstract

Political scientists frequently use instrumental variables (IV) estimation to estimate the causal effect of an endogenous treatment variable. However, when the treatment effect is heterogeneous, this estimation strategy only recovers the local average treatment effect (LATE). The LATE is an average treatment effect (ATE) for a subset of the population: units that receive treatment if and only if they are induced by an exogenous IV. However, researchers may instead be interested in the ATE for the entire population of interest. In this article, we develop a simple reweighting method for estimating the ATE, shedding light on the identification challenge posed in moving from the LATE to the ATE. We apply our method to two published experiments in political science in which we demonstrate that the LATE has the potential to substantively differ from the ATE.

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Corresponding author
e-mail: peter.aronow@yale.edu (corresponding author)
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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

Alberto Abadie . 2002. Bootstrap tests for distributional treatment effects in instrumental variable models. Journal of the American Statistical Association 97(457): 284–92.

Joshua D. Angrist , and Guido W. Imbens 1995. Two-stage least squares estimation of average causal effects in models with variable treatment intensity. Journal of the American Statistical Association 90(430): 431–42.

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Donald P. Green , Alan S. Gerber , and David W. Nickerson 2003. Getting out the vote in local elections: Results from six door-to-door canvassing experiments. Journal of Politics 65(4): 1083–96.

James J. Heckman , and Sergio Urzua . 2010. Comparing IV with structural models: What simple IV can and cannot identify. Journal of Econometrics 156(1): 2737.

Marshall M. Joffe , and Colleen Brensinger . 2003. Weighting in instrumental variables and G-estimation. Statistics in Medicine 22(1): 1285–303.

Marshall M. Joffe , Thomas R. Ten Have , and Colleen Brensinger . 2003. The compliance score as a regressor in randomized trials. Biostatistics 4(3): 327–40.

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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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