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Weighting Regressions by Propensity Scores

Freedman, David A., Berk, Richard A.·2008·Evaluation Review

Abstract

Regressions can be weighted by propensity scores in order to reduce bias. However, weighting is likely to increase random error in the estimates, and to bias the estimated standard errors downward, even when selection mechanisms are well understood. Moreover, in some cases, weighting will increase the bias in estimated causal parameters. If investigators have a good causal model, it seems better just to fit the model without weights. If the causal model is improperly specified, there can be significant problems in retrieving the situation by weighting, although weighting may help under some circumstances.

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Metadata

Authors
Freedman, D. A., & Berk, R. A.
Venue
Evaluation Review
Published
2008-08
Volume
32
Issue
4
Pages
392-409
ISSN
  • 0145-4692 (ISSN-L)
  • 0193-841X
  • 1552-3926
OpenAlex
W2027515467
Cited by
251
References
3
FWCI
9.00
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Weighting Regressions by Propensity Scores. https://app.cochat.ai/papers/10.1177/0193841x08317586
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Freedman, D. A., & Berk, R. A. (2008). Weighting Regressions by Propensity Scores. Evaluation Review. https://doi.org/10.1177/0193841x08317586

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