konfound: Command to quantify robustness of causal inferences

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

Research output: Contribution to journalArticle

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 languageEnglish (US)
Pages (from-to)523-550
Number of pages28
JournalStata Journal
Volume19
Issue number3
DOIs
StatePublished - Sep 1 2019

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Causal Inference
Quantify
Robustness
Causal Model
Robustness Analysis
Public Policy
Social Sciences
Statistical method
Percent
Statistics
Necessary
Series
Methodology
Evaluation
Education

Keywords

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

ASJC Scopus subject areas

  • Mathematics (miscellaneous)

Cite this

konfound : Command to quantify robustness of causal inferences. / Xu, Ran; Frank, Kenneth A.; Maroulis, Spiro J.; Rosenberg, Joshua M.

In: Stata Journal, Vol. 19, No. 3, 01.09.2019, p. 523-550.

Research output: Contribution to journalArticle

Xu, Ran ; Frank, Kenneth A. ; Maroulis, Spiro J. ; Rosenberg, Joshua M. / konfound : Command to quantify robustness of causal inferences. In: Stata Journal. 2019 ; Vol. 19, No. 3. pp. 523-550.
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