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Matching is a conceptually straightforward method to make groups of units comparable on observed characteristics. The method is, however, limited to settings where the study design is simple and the sample is moderately sized. We illustrate these limitations by asking what the causal effects would have been if a large-scale voter mobilization experiment that took place in Michigan for the 2006 election were scaled up to the full population of registered voters. Matching could help us answer this question, but no existing matching method can accommodate the six treatment arms and the 6,762,701 observations involved in the study. To offer a solution for this and similar empirical problems, we introduce a generalization of the full matching method that can be used with any number of treatment conditions and complex compositional constraints. The associated algorithm produces near-optimal matchings; the worst-case maximum within-group dissimilarity is guaranteed to be no more than four times greater than the optimal solution, and simulation results indicate that it comes considerably closer to the optimal solution on average. The algorithm's ability to balance the treatment groups does not sacrifice speed, and it uses little memory, terminating in linearithmic time using linear space. This enables investigators to construct well-performing matchings within minutes even in complex studies with samples of several million units. [ABSTRACT FROM AUTHOR]

Natural experiments help to overcome some of the obstacles researchers face when making causal inferences in the social sciences. However, even when natural interventions are randomly assigned, some of the treatment–control comparisons made available by natural experiments may not be valid. We offer a framework for clarifying the issues involved, which are subtle and often overlooked. We illustrate our framework by examining four different natural experiments used in the literature. In each case, random assignment of the intervention is not sufficient to provide an unbiased estimate of the causal effect. Additional assumptions are required that are problematic. For some examples, we propose alternative research designs that avoid these conceptual difficulties. [ABSTRACT FROM PUBLISHER]

Following David Lee's pioneering work, numerous scholars have applied the regression discontinuity (RD) design to popular elections. Contrary to the assumptions of RD, however, we show that bare winners and bare losers in U.S. House elections (1942–2008) differ markedly on pretreatment covariates. Bare winners possess large ex ante financial, experience, and incumbency advantages over their opponents and are usually the candidates predicted to win by Congressional Quarterly's pre-election ratings. Covariate imbalance actually worsens in the closest House elections. National partisan tides help explain these patterns. Previous works have missed this imbalance because they rely excessively on model-based extrapolation. We present evidence suggesting that sorting in close House elections is due mainly to activities on or before Election Day rather than postelection recounts or other manipulation. The sorting is so strong that it is impossible to achieve covariate balance between matched treated and control observations, making covariate adjustment a dubious enterprise. Although RD is problematic for postwar House elections, this example does highlight the design's advantages over alternatives: RD's assumptions are clear and weaker than model-based alternatives, and their implications are empirically testable. [ABSTRACT FROM PUBLISHER]

Numerous studies show that the rate at which African-Americans cast ballots with missing or invalid votes, i.e., the African-American residual vote rate, is higher than the corresponding white rate. While existing literature argues that the plethora of African-American residual votes is caused by administrative problems or socioeconomic factors, we show using precinct-level data from two recent elections in Cook County, Illinois, that the African-American residual vote rate in electoral contests with black candidates is less than half the rate in contests without black candidates. African Americans, therefore, are able to reduce their residual vote rate when they wish to do so. We present complementary findings for white voters, whose residual vote rate often substantially increases in contests which feature dominant black candidates. [ABSTRACT FROM AUTHOR]

Investigates whether the butterfly ballot caused several thousand Al Gore supporters to vote mistakenly for Pat Buchanan in Palm beach County, Florida. Evidence of problems with ballots, voting machines or election administration; Source of the Buchanan's support in Palm Beach County; Effect of the ballot format leading to systematic biased voting errors.