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48
Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R,”Forthcoming
 Journal of Statistical Software
, 2008
"... Matching is an R package which provides functions for multivariate and propensity score matching and for finding optimal covariate balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance actually has been obtained are provided. The underl ..."
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Cited by 27 (5 self)
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Matching is an R package which provides functions for multivariate and propensity score matching and for finding optimal covariate balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance actually has been obtained are provided. The underlying matching algorithm is written in C++, makes extensive use of system BLAS and scales efficiently with dataset size. The genetic algorithm which finds optimal balance is parallelized and can make use of multiple CPUs or a cluster of computers. A large number of options are provided which control exactly how the matching is conducted and how balance is evaluated.
The dangers of extreme counterfactuals
 Political Analysis
, 2006
"... We address the problem that occurs when inferences about counterfactuals—predictions, ‘‘whatif’ ’ 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 ..."
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Cited by 13 (7 self)
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We address the problem that occurs when inferences about counterfactuals—predictions, ‘‘whatif’ ’ 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 allow more model dependence and then develop easytoapply 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. Free software that accompanies this article implements all the methods developed. 1
When can history be our guide? The pitfalls of counterfactual inference
 International Studies Quarterly
, 2007
"... 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 wellspecified statistical analyses become based on speculation and conve ..."
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Cited by 12 (5 self)
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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 wellspecified 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 modeldependence, so this problem can be hard to detect. We develop easytoapply 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 evidence in the data. For some research questions, history contains insufficient information to be our guide. Free software that accompanies this paper implements all our suggestions. Social science is about making inferencesFusing facts we know to learn about facts we do not know. Some inferential targets (the facts we do not know) are factual, which means that they exist even if we do not know them. In early 2003, Saddam Hussein was obviously either alive or dead, but the world did not know which it was
2009. From association to causation via a potential outcomes approach
 Informat. Systems Res
"... doi 10.1287/isre.1080.0184 ..."
Causal reasoning with ancestral graphs
, 2008
"... Causal reasoning is primarily concerned with what would happen to a system under external interventions. In particular, we are often interested in predicting the probability distribution of some random variables that would result if some other variables were forced to take certain values. One promin ..."
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Cited by 6 (0 self)
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Causal reasoning is primarily concerned with what would happen to a system under external interventions. In particular, we are often interested in predicting the probability distribution of some random variables that would result if some other variables were forced to take certain values. One prominent approach to tackling this problem is based on causal Bayesian networks, using directed acyclic graphs as causal diagrams to relate postintervention probabilities to preintervention probabilities that are estimable from observational data. However, such causal diagrams are seldom fully testable given observational data. In consequence, many causal discovery algorithms based on datamining can only output an equivalence class of causal diagrams (rather than a single one). This paper is concerned with causal reasoning given an equivalence class of causal diagrams, represented by a (partial) ancestral graph. We present two main results. The first result extends Pearl (1995)’s celebrated docalculus to the context of ancestral graphs. In the second result, we focus on a key component of Pearl’s calculus—the property of invariance under interventions, and give stronger graphical conditions for this property than those implied by the first result. The second result also improves the earlier, similar results due to Spirtes et al. (1993).
A Propensity Score Reweighting Approach to Estimating the Partisan Effects of Full Turnout
 in American Presidential Elections.” Political Analysis
, 2004
"... Borrowing an approach from the literature on the economics of discrimination, we estimate the impact of nonvoters on the outcome of presidential elections from 1952–2000 using data from the National Election Study (NES). Our estimates indicate that nonvoters are, on average, slightly more likely to ..."
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Cited by 4 (0 self)
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Borrowing an approach from the literature on the economics of discrimination, we estimate the impact of nonvoters on the outcome of presidential elections from 1952–2000 using data from the National Election Study (NES). Our estimates indicate that nonvoters are, on average, slightly more likely to support the Democratic Party. Of the 13 presidential elections between 1952 and 2000 we find no change in the eventual outcome of the election with two possible exceptions: 1980 and 2000. Thus our results are not all that dissimilar from other research on participation. Higher turnout in the form of compulsory voting would not radically change the partisan distribution of the vote. When elections are sufficiently close, however, a two percentage point increase may suffice to affect the outcome. Limitations of the NES data we use suggest that our estimates underestimate the impact of nonparticipation. We also compare our method with other econometric techniques. Finally, using our findings we speculate as to why the Democratic Party fails to undertake widespread ‘‘get out the vote’ ’ or registration drives. 1
Regression and causation: A critical examination of econometrics textbooks
 Mimeo., UCLA Cognitive Systems Laboratory
, 2012
"... This report surveys six influential econometric textbooks in terms of their mathematical treatment of causal concepts. It highlights conceptual and notational differences among the authors and points to areas where they deviate significantly from modern standards of causal analysis. We find that eco ..."
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Cited by 4 (1 self)
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This report surveys six influential econometric textbooks in terms of their mathematical treatment of causal concepts. It highlights conceptual and notational differences among the authors and points to areas where they deviate significantly from modern standards of causal analysis. We find that econonometric textbooks vary from complete denial to partial acceptance of the causal content of econometric equations and, uniformly, fail to provide coherent mathematical notation that distinguishes causal from statistical concepts. This survey also provides a panoramic view of the state of causal thinking in econometric education which, to the best of our knowledge, has not been surveyed before. 1
Adjusting for TimeVarying Confounding in Survival Analysis: A Technical Report." Population Studies Center Research Report 04
, 2004
"... ..."
Causal inference based on counterfactuals
 BMC MEDICAL RESEARCH METHODOLOGY
, 2005
"... Background
The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies.
Discussion
This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when ..."
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Cited by 1 (0 self)
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Background
The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies.
Discussion
This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, timevarying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures.
Summary
Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.
Otis Dudley Duncan’s Legacy: The Demographic Approach to Quantitative Reasoning in Social Science *
"... Schuman for comments on an earlier version of this paper. ..."
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Schuman for comments on an earlier version of this paper.