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konfound: Command to quantify robustness of causal inferences

Author

Listed:
  • Ran Xu

    (Virginia Tech)

  • Kenneth A. Frank

    (Michigan State University)

  • Spiro J. Maroulis

    (Arizona State University)

  • Joshua M. Rosenberg

    (University of Tennessee)

Abstract

Statistical methods that quantify the discourse about causal inferences in terms of possible sources of biases are becoming increasingly impor- tant to many social-science fields such as public policy, sociology, and education. These methods are also known as “robustness or sensitivity analyses”. A series of recent works (Frank [2000, Sociological Methods and Research 29: 147–194]; Pan and Frank [2003, Journal of Educational and Behavioral Statistics 28: 315– 337]; Frank and Min [2007, Sociological Methodology 37: 349–392]; and Frank et al. [2013, Educational Evaluation and Policy Analysis 35: 437–460]) on robust- ness analysis extends earlier methods. We implement these recent developments in Stata. In particular, we provide commands to quantify the percent bias nec- essary to invalidate an inference from a Rubin causal model framework and the robustness of causal inferences in terms of correlations associated with unobserved variables.

Suggested Citation

  • Ran Xu & Kenneth A. Frank & Spiro J. Maroulis & Joshua M. Rosenberg, 2019. "konfound: Command to quantify robustness of causal inferences," Stata Journal, StataCorp LP, vol. 19(3), pages 523-550, September.
    • Handle: RePEc:tsj:stataj:v:19:y:2019:i:3:p:523-550
    DOI: 10.1177/1536867X19874223
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-3/st0565/
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