Inferences about counterfactuals are essential for prediction,
answering what if questions, and estimating causal effects.
However, when the counterfactuals posed are too far from the data at
hand, conclusions drawn from well-specified statistical analyses
become based largely on speculation hidden in convenient modeling
assumptions that few would be willing to defend. Unfortunately,
standard statistical approaches assume the veracity of the model
rather than revealing the degree of model-dependence, which makes this
problem hard to detect. WhatIf offers easy-to-apply methods to
evaluate counterfactuals that do not require sensitivity testing over
specified classes of models. If an analysis fails the tests offered
here, then we know that substantive inferences will be sensitive to at
least some modeling choices that are not based on empirical evidence,
no matter what method of inference one chooses to use. WhatIf
implements the methods for evaluating counterfactuals discussed in
Gary King and Langche Zeng, 2006, "The Dangers of Extreme
Counterfactuals," Political Analysis 14 (2)
|Author||Christopher Gandrud [aut, cre], Gary King [aut], Ben Sabath [ctb], Heather Stoll [aut], Langche Zeng [aut]|
|Date of publication||2017-07-25 11:36:40 UTC|
|Maintainer||Christopher Gandrud <firstname.lastname@example.org>|
|License||GPL (>= 3)|
|Package repository||View on CRAN|
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