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Causal inference with observational data: Regression discontinuity and other methods in Stata

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  • Austin Nichols

    (Urban Institute)

Abstract

This overview of implementing quasi-experimental methods of estimating causal impacts (panel methods, matching estimators, instrumental variables, and regression discontinuity) emphasizes practical considerations and Stata-specific approaches. I pay particular attention to the regression discontinuity method, which seems to be less well-known in the larger community of Stata users, but is the most well regarded of the quasi-experimental methods in those circumstances where it is appropriate. We will also examine some examples of hybrid applications of these estimation techniques.

Suggested Citation

  • Austin Nichols, 2009. "Causal inference with observational data: Regression discontinuity and other methods in Stata," German Stata Users' Group Meetings 2009 02, Stata Users Group.
    • Handle: RePEc:boc:dsug09:02
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    File URL: http://fmwww.bc.edu/repec/dsug2009/nichols.pdf
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    Cited by:

    1. Jonathan Meer & Jeremy West, 2016. "Effects of the Minimum Wage on Employment Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 51(2), pages 500-522.
    2. Christian K.M. Kingombe, 2012. "The Linkage between Outcome Differences in Cotton Production and Rural Roads Improvements - A Matching Approach," IHEID Working Papers 12-2012, Economics Section, The Graduate Institute of International Studies.

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