konfound: Command to quantify robustness of causal inferences

Ran Xu, Kenneth A. Frank, Spiro J. Maroulis, Joshua M. Rosenberg

Research output: Contribution to journalArticlepeer-review

64 Scopus citations


Statistical methods that quantify the discourse about causal inferences in terms of possible sources of biases are becoming increasingly important 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 robustness analysis extends earlier methods. We implement these recent developments in Stata. In particular, we provide commands to quantify the percent bias necessary to invalidate an inference from a Rubin causal model framework and the robustness of causal inferences in terms of correlations associated with unobserved variables.

Original languageEnglish (US)
Pages (from-to)523-550
Number of pages28
JournalStata Journal
Issue number3
StatePublished - Sep 1 2019


  • bias
  • causal inferences
  • confounding
  • konfound
  • mkonfound
  • pkonfound
  • robustness or sensitivity analyses
  • st0565

ASJC Scopus subject areas

  • Mathematics (miscellaneous)


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