The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Weighting Regressions by Propensity Scores
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hirano, K., & Imbens, G. W. (2001) · Health Services and Outcomes Research Methodology · doi:10.1023/a:1020371312283 · Cited by 1,039
On The So-Called “Huber Sandwich Estimator” and “Robust Standard Errors”
Freedman, D. A. (2006) · The American Statistician · doi:10.1198/000313006x152207 · Cited by 498
Self-citationNonexperimental Versus Experimental Estimates of Earnings Impacts
Glazerman, S., Levy, D. M., & Myers, D. (2003) · The ANNALS of the American Academy of Political and Social Science · doi:10.1177/0002716203254879 · Cited by 352
Cited by
Covariate Balancing Propensity Score
Imai, K., & Ratkovic, M. (2014) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12027 · Cited by 1,315
Diagnosing and responding to violations in the positivity assumption
Petersen, M. L., Porter, K. E., Gruber, S., Wang, Y., & van der Laan, M. J. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280210386207 · Cited by 548
Causal Inference in Sociological Research
Gangl, M. (2010) · Annual Review of Sociology · doi:10.1146/annurev.soc.012809.102702 · Cited by 401
Braga, A. A., Piehl, A. M., & Hureau, D. (2009) · Journal of Research in Crime and Delinquency · doi:10.1177/0022427809341935 · Cited by 142
Gaddis, S. M. (2012) · Social Forces · doi:10.1093/sf/sos003 · Cited by 84
Metadata
- Authors
- Freedman, D. A., & Berk, R. A.
- Venue
- Evaluation Review
- Publisher
- SAGE Publications
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Weighting Regressions by Propensity Scores. https://app.cochat.ai/papers/10.1177/0193841x08317586
- The paper
- Freedman, D. A., & Berk, R. A. (2008). Weighting Regressions by Propensity Scores. Evaluation Review. https://doi.org/10.1177/0193841x08317586
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.