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 wellspecified 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 modeldependence, which makes this problem hard to detect. WhatIf offers easytoapply 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.
Package details 


Author  Christopher Gandrud [aut, cre], Gary King [aut], Heather Stoll [aut], Langche Zeng [aut] 
Date of publication  20170321 15:20:18 UTC 
Maintainer  Christopher Gandrud <zelig.zee@gmail.com> 
License  GPL (>= 3) 
Version  1.58 
URL  http://gking.harvard.edu/whatif 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.