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Below results based on the criteria 'Counterfactual'
Total number of records returned: 4
Spatial Voting Theory and Counterfactual Inference: John C. Breckenridge and the Presidential Election of 1860
Jenkins, Jeffery A.
spatial voting theory
One important catalyst for the onset of the Civil War was the presidential election of Abraham Lincoln in 1860. Lincoln, competing against three other candidates, won election with the smallest percentage of the popular vote in American history. Given the circumstances, a slightly different electoral slate might have engineered his defeat. We examine this possibility by focusing on the candidacy of John C. Breckinridge, the final entrant into the race. Historians disagree over the rationale behind Breckinridge's candidacy. Some argue that it was a desperate effort to defeat Lincoln; others suggest that it was designed to insure Lincoln's victory. Using election counterfactuals and applying spatial voting theory, we examine these arguments. Our evidence suggests that Breckinridge had no reasonable chance to win. Support for Breckinridge's candidacy was only reasonable if the intention were to elect Lincoln.
How Factual is your Counterfactual?
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. Yet, standard model outputs do not reveal the degree of model-dependence, and so this problem can be hard to detect, regardless of its severity. We develop easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models. One analysis with these methods applies to the class of all models, for any smooth conditional expectation function, and to the set of all possible dependent variables, given only the choice of a set of explanatory variables. We illustrate by studying the scholarly literatures that try to assess the effects of changes in the degree of democracy in a country (on any dependent variable); we find widespread evidence that scholars are inadvertently drawing conclusions based more on their hypotheses than on their empirical evidence.
The Dangers of Extreme Counterfactuals
We address the problem that occurs when inferences about counterfactuals -- predictions, ``what if'' questions, and causal effects -- are attempted far from the available data. The danger of these extreme counterfactuals is that substantive conclusions drawn from statistical models that fit the data well turn out to be based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend. Yet existing statistical strategies provide few reliable means of identifying extreme counterfactuals. We offer a proof that inferences farther from the data are more model-dependent, and then develop easy-to-apply methods to evaluate how model-dependent our answers would be to specified counterfactuals. These methods require neither sensitivity testing over specified classes of models nor evaluating any specific modeling assumptions. If an analysis fails the simple tests we offer, then we know that substantive results are sensitive to at least some modeling choices that are not based on empirical evidence. The most recent version of this paper and software that implements the methods described is available at http://gking.harvard.edu.
Is It Worth Going the Extra Mile to Improve Causal Inference? Understanding Voting in Los Angeles County
Brady, Henry E.
Two seemingly unrelated approaches to quantitative analysis have recently become more popular in social science applications. The first approach is the explicit consideration of counterfactuals in causal inference and the development of various matching techniques to choose control cases comparable to treated cases in terms of some predefined characteristics. To be useful, these methods require the identification of important characteristics that are likely to ensure that a statistical condition called “conditional independence” is met. The second trend is the increased attention given to geography and the use of spatial statistics. Although these two approaches have found their ways into the social science research separately, we think that they can be fruitfully combined. Geography and Geographic Information Systems (GIS) can improve matching and causal inference. Geography can be conceptualized in terms of “distance” and “place” which can provide guidance about potentially important characteristics that can be used to improve matching. After developing a conceptual framework that shows how this can be done, we present two empirical examples which combine counterfactual thinking with geographical ideas. The first example looks at the cost of voting and demonstrates the utility of matching using zip codes and distance to polling place. The second example looks at the performance of the InkaVote voting system in Los Angeles by matching precincts in LA with geographically adjacent precincts in surrounding counties. This example demonstrates the strengths and weaknesses of geographic proximity as a matching variable. In pursuing these examples, we also show how recent progress in GIS techniques provides tools that can deepen researchers’ understanding of their idea.