A critical appraisal of propensity‐score matching in the medical literature between 1996 and 2003

By Peter C. Austin. Statistics in Medicine, 2008

1,286 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d7b1468d21cd8fb46b25bf1608edb7d04e27c590

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Citations in the corpus

Legume-based rotations have clear economic advantages over cereal monocropping in dry areas (2019, background): “This leads to confounding errors (see Austin 2008 for a critical review of PSM).”
Why Propensity Scores Should Not Be Used for Matching (2019, background): “…Hill, Stefano Iacus, Kosuke Imai, Simon Jackman, John Londregan, Adam Meirowitz, Giuseppe Porro, Molly Roberts, Jamie Robins, Bradley Spahn, Brandon Stewart, Liz Stuart, Chris Winship, and Yiqing Xu for helpful suggestions, and Connor Jerzak, Chris Lucas, Jason Sclar for superb research assistance.”
Challenges With Propensity Score Strategies in a High-Dimensional Setting and a Potential Alternative (2011, background): “Most scholars reject the idea that statistical tests are useful in this setting (for a discussion, see Austin, 2008; for dissenting ideas, see Hansen, 2008).”
Bayesian Nonparametric Modeling for Causal Inference (2011, background): “For example, in his review of 47 articles published in the medical literature between 1996 and 2003 that use propensity score matching, Austin (2008) found that 17 did not present any balance results whatsoever and that all but two used methods that arguably were inappropriate for at least some…”
Homelessness and Reentry (2014, background): “Austin, P. (2008). A critical appraisal of propensity score matching in the medical literature between 1996 and 2003. Statistics in Medicine, 27, 2037-2049. Barnoski, R., & Drake, E. k. (2007). Washington’s Offender Accountability Act: Department of Corrections’ Static Risk Assessment. Olympia: Wash”
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies (2011, methodology): “When outcomes are dichotomous, the number needed treat (NNT), the reciprocal of the absolute risk reduction, denotes the number of subjects that one must treat to avoid the occurrence of one event.”