Journal
STATA JOURNAL
Volume 19, Issue 3, Pages 523-550Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/1536867X19874223
Keywords
st0565; konfound; mkonfound; pkonfound; causal inferences; bias; confounding; robustness or sensitivity analyses
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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.
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