konfound: Command to quantify robustness of causal inferences

Ran Xu (), Kenneth A. Frank (), Spiro J. Maroulis () and Joshua M. Rosenberg ()
Additional contact information
Ran Xu: Virginia Tech
Kenneth A. Frank: Michigan State University
Spiro J. Maroulis: Arizona State University
Joshua M. Rosenberg: University of Tennessee

Stata Journal, 2019, vol. 19, issue 3, 523-550

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.

Keywords: konfound; mkonfound; pkonfound; causal inferences; bias; con- founding; robustness or sensitivity analyses (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1177/1536867X19874223

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