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An introduction to matching methods for causal inference and their implementation in Stata

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  • Barbara Sianesi

    (Institute for Fiscal Studies)

Abstract

Matching, especially in its propensity-score flavors, has become an extremely popular evaluation method. Matching is, in fact, the best-available method for selecting a matched (or reweighted) comparison group that looks like the treatment group of interest. In this talk, I will introduce matching methods within the general problem of causal inference, highlight their strengths and weaknesses, and offer a brief overview of different matching estimators. Using psmatch2, I will then step through a practical example in Stata that is based on real data. I will then show how to implement some of these estimators, as well as highlight a number of implementational issues.

Suggested Citation

  • Barbara Sianesi, 2010. "An introduction to matching methods for causal inference and their implementation in Stata," German Stata Users' Group Meetings 2010 02, Stata Users Group.
    • Handle: RePEc:boc:dsug10:02
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    Cited by:

    1. Renata Baborska & Emilio Hernandez & Emiliano Magrini & Cristian Morales-Opazo, 2020. "The impact of financial inclusion on rural food security experience: A perspective from low-and middle-income countries," Review of Development Finance Journal, Chartered Institute of Development Finance, vol. 10(2), pages 1-18.
    2. Haeng-Sun KIM, 2016. "Firms' leverage and export market participation: Evidence from South Korea," International Economics, CEPII research center, issue 148, pages 41-58.
    3. Haeng-Sun Kim, 2016. "Firms’ Leverage and Export Market Participation: Evidence from South Korea," Working Papers halshs-01643899, HAL.
    4. Choi, HwaJung & Burgard, Sarah & Elo, Irma T. & Heisler, Michele, 2015. "Are older adults living in more equal counties healthier than older adults living in more unequal counties? A propensity score matching approach," Social Science & Medicine, Elsevier, vol. 141(C), pages 82-90.

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