What Would It Take to Change an Inference? Using Rubin’s Causal Model to Interpret the Robustness of Causal Inferences

By Kenneth A. Frank, Spiro J. Maroulis, M. Duong, Ben Kelcey. 2013

342 citations recorded by the source corpus.

Subfields: Education, Information Systems and Management, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dfcc4f6ce49f5d3ebede2c486417c18d95654ece

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In Statistics and Probability

1. John R. Busenbark
2. Daniel Gamache
3. Michael C. Withers
4. Hyunju Yoon

Citations in the corpus

• Omitted Variable Bias: Examining Management Research With the Impact Threshold of a Confounding Variable (ITCV) (2021, methodology): “…to highlight that the ITCV is useful to examine the causal inference and statistical significance of relationships between independent and dependent variables, but it does not address inaccuracies of the parameter estimate itself (Frank, 2000; Frank et al., 2013; Larcker & Rusticus, 2010).”