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Weighting Regressions by Propensity Scores

David A. Freedman and Richard A. Berk
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David A. Freedman: University of California, Berkeley, freedman@stat.berkeley.edu
Richard A. Berk: University of Pennsylvania, berkr@sas.upenn.edu

Evaluation Review, 2008, vol. 32, issue 4, 392-409

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.

Keywords: causation; selection; models; experiments; observational studies; regression; propensity scores (search for similar items in EconPapers)
Date: 2008
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