Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that it is possible to find a specification that fits the author’s favorite hypothesis? And how do we evaluate or even define statistical properties like unbiasedness or mean squared error when no unique model or estimator even exists? Matching methods, which offer the promise of causal inference with fewer assumptions, constitute one possible way forward, but crucial results in this fast-growing methodological

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: Two algorithms for producing multiple imputations for missing data are evaluated with simulated data. Software using a propensity score classifier with the approximate Bayesian boostrap produces badly biased estimates of regression coefficients when data on predictor variables are missing at random or missing completely at random. On the other hand, a regression-based method employing the data augmentation algorithm produces estimates with little or no bias. 4 Multiple imputation (MI) appears to be one of the most attractive methods for generalpurpose handling of missing data in multivariate analysis. The basic idea, first proposed by Rubin (1977) and elaborated in his (1987) book, is quite simple: 1. Impute missing values using an appropriate model that incorporates random variation. 2. Do this M times (usually 3-5 times), producing M "complete" data sets. 3. Perform the desired analysis on each data set using standard complete-data methods. 4. Average the values of the parameter ...

This article reviews multiple imputation, describes assumptions that it requires, and reviews software packages that implement this procedure. We apply the methods and compare the results using two examples---a child psychopathology dataset with missing outcomes and an artificial dataset with missing covariates. We conclude with some discussion of the strengths and weaknesses of these implementations as well as advantages and limitations of imputation

This paper provides new evidence on the determinants of individual trade-policy preferences using individual-level survey data for the United States. There are two main empirical results. First, we find that factor type dominates industry of employment in explaining support for trade barriers. Second, we find that home ownership also matters for individuals ' trade-policy preferences. Independent of factor type, home ownership in counties with a manufacturing mix concentrated in comparative-disadvantage industries is correlated with support for trade barriers. This finding suggests that in addition to current factor incomes driving preferences as in standard trade models, preferences also depend on asset values.

A key distinction among theories of civil war is between those that are built upon motivation and those that are built upon feasibility. We analyze a comprehensive global sample of civil wars for the period 1965-2004 and subject the results to a range of robustness tests. The data constitute a substantial advance on previous work. We find that variables that are close proxies for feasibility have powerful consequences for the risk of a civil war. Our results substantiate the 'feasibility hypothesis ' that where civil war is feasible it will occur without reference to motivation. 2 1.

1 You may be interested in our Anchoring Vignettes web site, which, as a companion to this paper, provides software to implement the methods here, answers to frequently asked questions, example vignettes, and other materials (see

We offer the first independent scholarly evaluation of the claims, forecasts, and causal inferences of the State Failure Task Force and their efforts to forecast when states will fail. This task force, set up at the behest of Vice President Gore in 1994, has been led by a group of distinguished academics working as consultants to the U.S. Central Intelligence Agency. State failure refers to the collapse of the authority of the central government to impose order, as in civil wars, revolutionary wars, genocides, politicides, and adverse or disruptive regime transitions. State Failure Task Force reports and publications have received attention in the media, in academia, and from public policy decision-makers. In this paper, we identify several methodological errors in the task force work that cause their reported forecast probabilities of conflict to be too large, their causal inferences to be biased in unpredictable directions, and their claims of forecasting performance to be exaggerate...

Abstract—This paper uses three years of individual-level data to analyze the determinants of individual preferences over immigration policy in the United States. We have two main empirical results. First, less-skilled workers are signi � cantly more likely to prefer limiting immigrant in � ows into the United States. Our � nding suggests that, over the time horizons that are relevant to individuals when evaluating immigration policy, individuals think that the U.S. economy absorbs immigrant in � ows at least partly by changing wages. Second, we � nd no evidence that the relationship between skills and immigration opinions is stronger in high-immigration communities. I.

We develop a general ontology of statistical methods and use it to propose a common framework for statistical analysis and software development built on and within the R language, including R’s numerous existing packages. This framework offers a simple unified structure and syntax that can encompass a large fraction of existing statistical procedures. We conjecture that it can be used to encompass and present simply a vast majority of existing statistical methods, without requiring changes in existing approaches, and regardless of the theory of inference on which they are based, notation with which they were developed, and programming syntax with which they have been implemented. This development enabled us, and should enable others, to design statistical software with a single, simple, and unified user interface that helps overcome the conflicting notation, syntax, jargon, and statistical methods existing across the methods subfields of numerous academic disciplines. The approach also enables one to build a graphical user interface that automatically includes any method encompassed within the framework. We hope that the result of this line of research will greatly reduce the time from the creation of a new statistical innovation to its widespread use by applied researchers whether or not they use or program in R.