Package: WhatIf
Version: 1.5-4
Date: 2007-09-24
Title: WhatIf: Software for Evaluating Counterfactuals
Author: Heather Stoll <hstoll@polsci.ucsb.edu>,
Gary King <king@harvard.edu>,
Langche Zeng <zeng@ucsd.edu>,
Maintainer: Heather Stoll <hstoll@polisci.ucsb.edu>
Depends: R (>= 2.3.1), lpSolve
Description: 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).
License: GPL version 2 or newer
URL: http://gking.harvard.edu/whatif
Packaged: Mon Sep 24 19:55:24 2007; king

Package: WhatIf
Version: 1.5-5
Date: 2009-03-03
Title: WhatIf: Software for Evaluating Counterfactuals
Author: Heather Stoll <hstoll@polsci.ucsb.edu>, Gary King
<king@harvard.edu>, Langche Zeng <zeng@ucsd.edu>,
Maintainer: Heather Stoll <hstoll@polisci.ucsb.edu>
Depends: R (>= 2.3.1), lpSolve
Description: 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).
License: GPL (>= 2)
URL: http://gking.harvard.edu/whatif
Packaged: Tue Mar 3 20:49:37 2009; king
Repository: CRAN
Date/Publication: 2009-03-08 16:10:18