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HandWiki. Generalizing the Local Average Treatment Effect. Encyclopedia. Available online: https://encyclopedia.pub/entry/31872 (accessed on 14 October 2026).
HandWiki. Generalizing the Local Average Treatment Effect. Encyclopedia. Available at: https://encyclopedia.pub/entry/31872. Accessed October 14, 2026.
HandWiki. "Generalizing the Local Average Treatment Effect" Encyclopedia, https://encyclopedia.pub/entry/31872 (accessed October 14, 2026).
HandWiki. (2022, October 30). Generalizing the Local Average Treatment Effect. In Encyclopedia. https://encyclopedia.pub/entry/31872
HandWiki. "Generalizing the Local Average Treatment Effect." Encyclopedia. Web. 30 October, 2022.
Generalizing the Local Average Treatment Effect
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The content is sourced from: https://handwiki.org/wiki/Medicine:Generalizing_the_local_average_treatment_effect

The local average treatment effect (LATE), also known as the complier average causal effect (CACE), refers to the treatment effect among compliers. The LATE may not be of the same value as the average treatment effect (ATE), so extrapolating the LATE directly should be done cautiously. In addition, the LATE retrieved from an experiment may not immediately be externally valid, especially when there is a case for treatment effect heterogeneity (i.e. the treatment effect varies across individuals).  However, generalizing the LATE through reweighting can be attempted, given certain key assumptions.

average treatment effect causal effect treatment effect

References

  1. Angrist, Joshua (September 1990). The Draft Lottery and Voluntary Enlistment in the Vietnam Era. Cambridge, MA. doi:10.3386/w3514.  https://dx.doi.org/10.3386%2Fw3514
  2. Imbens, Guido W.; Angrist, Joshua D. (March 1994). "Identification and Estimation of Local Average Treatment Effects". Econometrica 62 (2): 467. doi:10.2307/2951620. ISSN 0012-9682. http://www.nber.org/papers/t0118.pdf. 
  3. Deaton, Angus (January 2009). Instruments of development: Randomization in the tropics, and the search for the elusive keys to economic development. Cambridge, MA. doi:10.3386/w14690.  https://dx.doi.org/10.3386%2Fw14690
  4. Heckman, James J.; Urzúa, Sergio (May 2010). "Comparing IV with structural models: What simple IV can and cannot identify". Journal of Econometrics 156 (1): 27–37. doi:10.1016/j.jeconom.2009.09.006. ISSN 0304-4076. PMID 20440375.  http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2861784
  5. Aronow, Peter M.; Carnegie, Allison (2013). "Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental Variable". Political Analysis 21 (4): 492–506. doi:10.1093/pan/mpt013. ISSN 1047-1987.  https://dx.doi.org/10.1093%2Fpan%2Fmpt013
  6. Imbens, Guido W (June 2010). "Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009)". Journal of Economic Literature 48 (2): 399–423. doi:10.1257/jel.48.2.399. ISSN 0022-0515. http://www.nber.org/papers/w14896.pdf. 
  7. Kowalski, Amanda (2016). "Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments". NBER Working Paper No. 22363. doi:10.3386/w22363.  https://dx.doi.org/10.3386%2Fw22363
  8. Abadie, Alberto (March 2002). "Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable Models". Journal of the American Statistical Association 97 (457): 284–292. doi:10.1198/016214502753479419. ISSN 0162-1459.  https://dx.doi.org/10.1198%2F016214502753479419
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