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Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies

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  • Donald B. Rubin

Abstract

Inference for causal effects is a critical activity in many branches of science and public policy. The field of statistics is the one field most suited to address such problems, whether from designed experiments or observational studies. Consequently, it is arguably essential that departments of statistics teach courses in causal inference to both graduate and undergraduate students. This article discusses an outline of such courses based on repeated experience over more than a decade.

Suggested Citation

  • Donald B. Rubin, 2004. "Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies," Journal of Educational and Behavioral Statistics, , vol. 29(3), pages 343-367, September.
    • Handle: RePEc:sae:jedbes:v:29:y:2004:i:3:p:343-367
    DOI: 10.3102/10769986029003343
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    Citations

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    Cited by:

    1. Yu-Jen Cheng & Mei-Cheng Wang, 2015. "Causal estimation using semiparametric transformation models under prevalent sampling," Biometrics, The International Biometric Society, vol. 71(2), pages 302-312, June.
    2. Gargani, John, 2017. "The leap from ROI to SROI: Farther than expected?," Evaluation and Program Planning, Elsevier, vol. 64(C), pages 116-126.
    3. Hohenleitner, Ingrid & Hillmann, Katja, 2019. "Impact of welfare sanctions on employment and benefit receipt: Considering top-up benefits and indirect sanctions," HWWI Research Papers 189, Hamburg Institute of International Economics (HWWI).
    4. Armstrong, Christopher S. & Kepler, John D., 2018. "Theory, research design assumptions, and causal inferences," Journal of Accounting and Economics, Elsevier, vol. 66(2), pages 366-373.
    5. Tenglong Li & Kenneth A. Frank, 2019. "On the probability of a causal inference is robust for internal validity," Papers 1906.08726, arXiv.org.
    6. Eunah Jung & Heeyeun Yoon, 2018. "Is Flood Risk Capitalized into Real Estate Market Value? A Mahalanobis-Metric Matching Approach to the Housing Market in Gyeonggi, South Korea," Sustainability, MDPI, vol. 10(11), pages 1-17, November.
    7. Rüdiger Mutz & Hans-Dieter Daniel, 2012. "The generalized propensity score methodology for estimating unbiased journal impact factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 92(2), pages 377-390, August.

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