Abstract
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 language | English (US) |
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Pages (from-to) | 523-550 |
Number of pages | 28 |
Journal | Stata Journal |
Volume | 19 |
Issue number | 3 |
DOIs | |
State | Published - Sep 1 2019 |
Keywords
- bias
- causal inferences
- confounding
- konfound
- mkonfound
- pkonfound
- robustness or sensitivity analyses
- st0565
ASJC Scopus subject areas
- Mathematics (miscellaneous)