{
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  "Package": "WhatIf",
  "Title": "Software for Evaluating Counterfactuals",
  "Version": "1.5-10",
  "Date": "2020-11-08",
  "Author": "Heather Stoll <hstoll@polsci.ucsb.edu>, Gary King\n<king@harvard.edu>, Langche Zeng <zeng@ucsd.edu>, Christopher\nGandrud <zelig.zee@gmail.com>, Ben Sabath",
  "Maintainer": "Soubhik Barari <soubhikbarari@gmail.com>",
  "Description": "Inferences about counterfactuals are essential for\nprediction, answering what if questions, and estimating causal\neffects. However, when the counterfactuals posed are too far\nfrom the data at hand, conclusions drawn from well-specified\nstatistical analyses become based largely on speculation hidden\nin convenient modeling assumptions that few would be willing to\ndefend. Unfortunately, standard statistical approaches assume\nthe veracity of the model rather than revealing the degree of\nmodel-dependence, which makes this problem hard to detect.\nWhatIf offers easy-to-apply methods to evaluate counterfactuals\nthat do not require sensitivity testing over specified classes\nof models. If an analysis fails the tests offered here, then we\nknow that substantive inferences will be sensitive to at least\nsome modeling choices that are not based on empirical evidence,\nno matter what method of inference one chooses to use. WhatIf\nimplements the methods for evaluating counterfactuals discussed\nin Gary King and Langche Zeng, 2006, \"The Dangers of Extreme\nCounterfactuals,\" Political Analysis 14 (2)\n<DOI:10.1093/pan/mpj004>; and Gary King and Langche Zeng, 2007,\n\"When Can History Be Our Guide? The Pitfalls of Counterfactual\nInference,\" International Studies Quarterly 51 (March)\n<DOI:10.1111/j.1468-2478.2007.00445.x>.",
  "License": "GPL (>=3)",
  "URL": "https://gking.harvard.edu/whatif",
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  "Date/Publication": "2023-05-17 20:50:25 UTC",
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