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 on speculation and convenient but indefensible model assumptions rather than empirical evidence. Unfortunately, standard statistical approaches assume the veracity of the model rather than revealing the degree of model-dependence, and so this problem can be hard to detect. We develop easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models. If an analysis fails the tests we offer, then we know that substantive results are sensitive to at least some modeling choices that are not based on empirical evidence. We use these methods to evaluate the extensive scholarly literatures on the effects of changes in the degree of democracy in a country (on any dependent variable) and separate analyses of the effects of UN peacebuilding efforts. We find evidence that many scholars are inadvertently drawing conclusions based more on modeling hypotheses than on their data. For some research questions, history contains insufficient information to be our guide.

You may also be interested in free software, called WhatIf, that implements all the methods in this paper, and an overlapping, companion paper to this one, entitled "The Dangers of Extreme Counterfactuals" Political Analysis, 14, 2, 2006, forthcoming (Paper: PDF | Abstract: HTML): it includes complete mathematical proofs, more general notation, and other technical material, but fewer examples and less pedagogical material. Also related is Daniel Ho, Kosuke Imai, Gary King, and Elizabeth Stuart, "Matching as Nonparametric Preprocessing for Improving Parametric Causal Inference," (Abstract: HTML | Software: MatchIt). Also see related research.