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Estimating average treatment effects from observational data using teffects

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  • David Drukker

    (StataCorp LP)

Abstract

After reviewing the potential-outcome framework for estimating treatment effects from observational data, this talk discusses how to estimate the average treatment effect and the average treatment effect on the treated using the regression-adjustment estimator, the inverse-probability-weighted estimator, two doubly robust estimators, and two matching estimators implemented in teffects.

Suggested Citation

  • David Drukker, 2014. "Estimating average treatment effects from observational data using teffects," German Stata Users' Group Meetings 2014 07, Stata Users Group.
    • Handle: RePEc:boc:dsug14:07
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    File URL: http://fmwww.bc.edu/RePEc/dsug2014/de14_drukker_teintro.pdf
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    References listed on IDEAS

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    1. Jeffrey M. Wooldridge, 2002. "Inverse probability weighted M-estimators for sample selection, attrition, and stratification," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 1(2), pages 117-139, August.
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    Cited by:

    1. Sayoni Roychowdhury & Indrila Ganguly & Abhik Ghosh, 2024. "Robust estimation of average treatment effects from panel data," Statistical Papers, Springer, vol. 65(1), pages 139-179, February.
    2. Adeyemo, T. & Okoruwa, V. & Akinyosoye, V., 2018. "Estimating causal effects of cassava based value-webs on smallholders welfare: a multivalued treatment approach," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277052, International Association of Agricultural Economists.
    3. Sayoni Roychowdhury & Indrila Ganguly & Abhik Ghosh, 2021. "Robust Estimation of Average Treatment Effects from Panel Data," Papers 2112.13228, arXiv.org, revised Dec 2022.

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