Skip to main content
    • Aa
    • Aa

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

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

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.

Corresponding author
e-mail: (corresponding author)
Linked references
Hide All

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.

Joshua D. Angrist , and Ivan Fernandez-Val . 2013. ExtrapoLATE-ing: External validity and overidentification in the LATE framework. In Advances in economics and econometrics: Tenth world congress, Vol. 3, eds. Manuel Arellano Daron Acemoglu , and Eddie Deke , 401–36. Cambridge: Cambridge University Press.

Angus Deaton . 2009. Instruments of development: Randomization in the tropics, and the search for the elusive keys to economic development. Proceedings of the British Academy, 2008 Lectures 162: 123–60.

Dean A. Follmann 2000. On the effect of treatment among would-be treatment compliers: An analysis of the multiple risk factor intervention trial. Journal of the American Statistical Association 95(452): 1101–9.

Constantine E. Frangakis , and Donald B. Rubin 1999. Addressing complications of intention-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes. Biometrika 86(2): 365–79.

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.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
Type Description Title
Supplementary Materials

Aronow and Carnegie supplementary material

 PDF (1.8 MB)
1.8 MB


Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 20 *
Loading metrics...

Abstract views

Total abstract views: 61 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 27th April 2017. This data will be updated every 24 hours.