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Cited by 868

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Causal Inference With General Treatment Regimes: Generalizing the Propensity Score

Imai, Kosuke, van Dyk, David A·2004·Journal of the American Statistical Association
DOI 10.1198/016214504000001187
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Abstract

In this article we develop the theoretical properties of the propensity function, which is a generalization of the propensity score of Rosenbaum and Rubin. Methods based on the propensity score have long been used for causal inference in observational studies; they are easy to use and can effectively reduce the bias caused by nonrandom treatment assignment. Although treatment regimes need not be binary in practice, the propensity score methods are generally confined to binary treatment scenarios. Two possible exceptions have been suggested for ordinal and categorical treatments. In this article we develop theory and methods that encompass all of these techniques and widen their applicability by allowing for arbitrary treatment regimes. We illustrate our propensity function methods by applying them to two datasets; we estimate the effect of smoking on medical expenditure and the effect of schooling on wages. We also conduct simulation studies to investigate the performance of our methods.

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Metadata

Authors
Imai, K., & van Dyk, D. A.
Venue
Journal of the American Statistical Association
Publisher
Informa UK Limited
Published
2004-09
Volume
99
Issue
467
Pages
854-866
ISSN
  • 0162-1459 (ISSN-L)
  • 1537-274X
DOI
10.1198/016214504000001187
OpenAlex
W2111078766
Cited by
868
References
3
FWCI
13.45
Type
journal article
Language
en
Open access
closed

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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.