The Dangers of Extreme Counterfactuals

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The Dangers of Extreme Counterfactuals

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Title: The Dangers of Extreme Counterfactuals
Author: King, Gary; Zeng, Langche

Note: Order does not necessarily reflect citation order of authors.

Citation: King, Gary, and Langche Zeng. 2006. The dangers of extreme counterfactuals. Political Analysis 14(2): 131-159.
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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 are more model-dependent, 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.
Published Version: doi:10.1093/pan/mpj004
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4215040

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  • FAS Scholarly Articles [7470]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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