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Cited by 540

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

The Dangers of Extreme Counterfactuals

Open access
King, Gary, Zeng, Langche·2006·Political Analysis
DOI 10.1093/pan/mpj004
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Abstract

We address the problem that occurs when inferences about counterfactuals—predictions, “what-if” questions, and causal effects—are attempted far from the available data. The danger of these extreme counterfactuals is that substantive conclusions drawn from statistical models that fit the data well turn out to be based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend. Yet existing statistical strategies provide few reliable means of identifying extreme counterfactuals. We offer a proof that inferences farther from the data allow more model dependence and then develop easy-to-apply methods to evaluate how model dependent our answers would be to specified counterfactuals. These methods require neither sensitivity testing over specified classes of models nor evaluating any specific modeling assumptions. If an analysis fails the simple tests we offer, then we know that substantive results are sensitive to at least some modeling choices that are not based on empirical evidence. Free software that accompanies this article implements all the methods developed.

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Metadata

Authors
King, G., & Zeng, L.
Venue
Political Analysis
Publisher
Cambridge University Press (CUP)
Published
2006
Volume
14
Issue
2
Pages
131-159
ISSN
  • 1047-1987 (ISSN-L)
  • 1476-4989
DOI
10.1093/pan/mpj004
OpenAlex
W2121275048
Cited by
540
References
11
FWCI
12.17
Type
journal article
Language
en
Open access
bronze

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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