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Cited by 2,395Advanced Causal Inference TechniquesEvaluation and Performance AssessmentSchool Choice and Performance

The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.

Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs

Dehejia, Rajeev H., Wahba, Sadek·1999·Journal of the American Statistical Association
DOI 10.1080/01621459.1999.10473858
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Abstract

This article uses propensity score methods to estimate the treatment impact of the National Supported Work (NSW) Demonstration, a labor training program, on postintervention earnings. We use data from Lalonde's evaluation of nonexperimental methods that combine the treated units from a randomized evaluation of the NSW with nonexperimental comparison units drawn from survey datasets. We apply propensity score methods to this composite dataset and demonstrate that, relative to the estimators that Lalonde evaluates, propensity score estimates of the treatment impact are much closer to the experimental benchmark estimate. Propensity score methods assume that the variables associated with assignment to treatment are observed (referred to as ignorable treatment assignment, or selection on observables). Even under this assumption, it is difficult to control for differences between the treatment and comparison groups when they are dissimilar and when there are many preintervention variables. The estimated propensity score (the probability of assignment to treatment, conditional on preintervention variables) summarizes the preintervention variables. This offers a diagnostic on the comparability of the treatment and comparison groups, because one has only to compare the estimated propensity score across the two groups. We discuss several methods (such as stratification and matching) that use the propensity score to estimate the treatment impact. When the range of estimated propensity scores of the treatment and comparison groups overlap, these methods can estimate the treatment impact for the treatment group. A sensitivity analysis shows that our estimates are not sensitive to the specification of the estimated propensity score, but are sensitive to the assumption of selection on observables. We conclude that when the treatment and comparison groups overlap, and when the variables determining assignment to treatment are observed, these methods provide a means to estimate the treatment impact. Even though propensity score methods are not always applicable, they offer a diagnostic on the quality of nonexperimental comparison groups in terms of observable preintervention variables.

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Crossref · 10.1080/01621459.1999.10473858

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Citations

Cites

Research works printed but not linked: 6

  1. Angrist J., 1990, American Economic Review, 80, 313
  2. Dehejia R. H., 1998, “Matching Methods for Estimating Causal Effects in Non-Experimental Studies,”
  3. 10.1080/01621459.1989.10478848 · DOI
  4. Lalonde R., 1986, American Economic Review, 76, 604
  5. 1983, Summary and Findings of the National Supported Work Demonstration
  6. Rubin D., 1977, Journal of Educational Statistics, 2, 1

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Metadata

Authors
Dehejia, R. H., & Wahba, S.
Affiliations
  • Columbia University (US)
  • Morgan Stanley (United States) (US)
ORCID
Dehejia, Rajeev H.
Venue
Journal of the American Statistical Association
Publisher
Informa UK Limited
Published
1999-12
Volume
94
Issue
448
Pages
1053-1062
ISSN
  • 0162-1459 (ISSN-L)
  • 1537-274X
DOI
10.1080/01621459.1999.10473858
OpenAlex
W3122948292
Cited by
2,395
References
44
Citations by year
2026: 43 · 2025: 56 · 2024: 66 · 2023: 80 · 2022: 97 · 2021: 110 · 2020: 103 · 2019: 98 · 2018: 126 · 2017: 119 · 2016: 122 · 2015: 130 · 2014: 141 · 2013: 116 · 2012: 120
FWCI
43.40
Type
journal article
Language
en
Open access
closed
Locations
Journal of the American Statistical Association (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Social Sciences › Public Administration › Evaluation and Performance Assessment
  • Social Sciences › Education › School Choice and Performance
Keywords
propensity score methods, program evaluation, selection on observables, stratification, matching, sensitivity analysis, treatment effect estimation

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