WhatIf (1.5-9)

0 users

Evaluate Counterfactuals.


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) ; and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The Pitfalls of Counterfactual Inference," International Studies Quarterly 51 (March) .

Maintainer: Christopher Gandrud
Author(s): Christopher Gandrud [aut, cre], Gary King [aut], Ben Sabath [ctb], Heather Stoll [aut], Langche Zeng [aut]

License: GPL (>= 3)

Uses: lpSolve, pbmcapply, Zelig, testthat
Reverse depends: MatchIt
Reverse suggests: MatchIt

Released over 2 years ago.

4 previous versions



  (0 votes)


  (0 votes)

Log in to vote.


No one has written a review of WhatIf yet. Want to be the first? Write one now.

Related packages:(20 best matches, based on common tags.)

Search for WhatIf on google, google scholar, r-help, r-devel.

Visit WhatIf on R Graphical Manual.