Propensity Score Estimation With Boosted Regression for Evaluating Causal Effects in Observational Studies.
Bibliographic Data
| ID | 23318883 |
|---|---|
| Authors | Daniel F Mccaffrey (0000-0003-1196-5273, RAND Corporation), Greg Ridgeway (0000-0001-6911-0804, RAND Corporation), Andrew R Morral (0000-0001-9091-3724, RAND Corporation) |
| Year | 2004 |
| Volume | 9 |
| Issue | 4 |
| Pages | 403-425 |
| Publication date | 2004-12-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Psychological Methods (JOURNAL) |
| Journal identifiers | ISSN: 1082-989X • E-ISSN: 1939-1463 |
| Publisher | American Psychological Association (APA) (PUBLISHER) |
| DOI | 10.1037/1082-989x.9.4.403 |
| PMID | 15598095 |
| OpenAlex | W2028040032 |
| Language | EN |
| Citations received | 207 |
| References cited | 43 |
Causal effect modeling with naturalistic rather than experimental data is challenging. In observational studies participants in different treatment conditions may also differ on pretreatment characteristics that influence outcomes. Propensity score methods can theoretically eliminate these confounds for all observed covariates, but accurate estimation of propensity scores is impeded by large numbers of covariates, uncertain functional forms for their associations with treatment selection, and other problems. This article demonstrates that boosting, a modern statistical technique, can overcome many of these obstacles. The authors illustrate this approach with a study of adolescent probationers in substance abuse treatment programs. Propensity score weights estimated using boosting eliminate most pretreatment group differences and substantially alter the apparent relative effects of adolescent substance abuse treatment.
Boosting (machine learning) · Covariate · Econometrics · Estimation · Machine learning · Observational study · Propensity score matching · Psychiatry · Regression · Regression analysis · Selection bias · Statistics · Substance abuse · Advanced Causal Inference Techniques · Computer Science · Mathematics · Psychology · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
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| Unique citing works | 207 |
|---|---|
| Citations per year | 9,86 |
| Citation span | 2005 - 2026 (22) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 185 |