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Cited by 710

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Identification and estimation of causal effects of multiple treatments under the conditional independence assumption

Lechner, Michael·2001·ZEW Economic Studies
DOI 10.1007/978-3-642-57615-7_3
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Abstract

The assumption that the assignment to treatments is ignorable conditional on attributes plays an important role in the applied statistic and econometric evaluation literature. Another term for it is conditional independence assumption (CIA). This paper discusses identification using CIA when there are more than two types of mutually exclusive treatments. It turns out that low dimensional balancing scores, similar to the ones valid in the case of only two treatments, exist and can be used for identification of various causal effects. Therefore, a comparable reduction of the dimension of the estimation problem is achieved and the approach retains its basic simplicity. The paper also outlines a matching estimator potentially suitable in that framework.

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Metadata

Authors
Lechner, M.
Venue
ZEW Economic Studies
Publisher
Physica-Verlag HD
Published
2001
Pages
43-58
ISSN
  • 1615-6781 (ISSN-L)
  • 1867-2027
DOI
10.1007/978-3-642-57615-7_3
OpenAlex
W1515828664
Cited by
710
References
30
FWCI
35.42
Type
book chapter
Language
en
Open access
closed

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CoChat. (2026). Citation check: Identification and estimation of causal effects of multiple treatments under the conditional independence assumption. https://app.cochat.ai/papers/10.1007/978-3-642-57615-7_3
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Lechner, M. (2001). Identification and estimation of causal effects of multiple treatments under the conditional independence assumption. ZEW Economic Studies. https://doi.org/10.1007/978-3-642-57615-7_3

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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.