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334
Matching methods for causal inference: A review and a look forward.
- Statistical science.
, 2010
"... Abstract When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the origina ..."
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Cited by 98 (1 self)
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Abstract When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970's, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine, and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods-or developing methods related to matching-do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
- STATISTICS & PROBABILITY LETTERS
, 2011
"... MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of cau ..."
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Cited by 92 (13 self)
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MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.
Do Get-Out-The-Vote Calls Reduce Turnout? The Importance of Statistical Methods for Field Experiments
- American Political Science Review
, 2005
"... In their landmark study of a field experiment, Gerber and Green (2000) found that get-out-the-vote calls reduce turnout by five percentage points. In this article, I introduce statistical methods that can uncover discrepancies between experimental design and actual implementation. The application of ..."
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Cited by 64 (16 self)
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In their landmark study of a field experiment, Gerber and Green (2000) found that get-out-the-vote calls reduce turnout by five percentage points. In this article, I introduce statistical methods that can uncover discrepancies between experimental design and actual implementation. The application of this methodology shows that Gerber and Green’s negative finding is caused by inadvertent deviations from their stated experimental protocol. The initial discovery led to revisions of the original data by the authors and retraction of the numerical results in their article. Analysis of their revised data, however, reveals new systematic patterns of implementation errors. Indeed, treatment assignments of the revised data appear to be even less randomized than before their corrections. To adjust for these problems, I employ a more appropriate statistical method and demonstrate that telephone canvassing increases turnout by five percentage points. This article demonstrates how statistical methods can find and correct complications of field experiments. Voter mobilization campaigns are a central part of democratic elections. In the 2000 general election, for example, the Democratic and Republican parties spent an estimated $100 million on
Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies
- American Political Science Review
, 2011
"... Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and ..."
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Cited by 52 (8 self)
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Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficulties, the study of causal mechanisms is too important to abandon. We make three contributions to improve research on causal mechanisms. First, we present a minimum set of assumptions required under standard designs of experimental and observational studies and develop a general algorithm for estimating causal mediation effects. Second, we provide a method for assessing the sensitivity of conclusions to potential violations of a key assumption. Third, we offer alternative research designs for identifying causal mechanisms under weaker assumptions. The proposed approach is illustrated using media framing experiments and incumbency advantage studies. Over the last couple of decades, social scientists have given greater attention to methodological issues related to causation. This trend has led to a growing number of laboratory, field, and survey
Misunderstandings among experimentalists and observationalists about causal inference
, 2007
"... We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fal-lacies of causal inference in experimental and observational research. These issues concern some of the most basic advantages and disadvantages of each basic research design. Problems include improper us ..."
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Cited by 51 (24 self)
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We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fal-lacies of causal inference in experimental and observational research. These issues concern some of the most basic advantages and disadvantages of each basic research design. Problems include improper use of hypothesis tests for covariate balance between the treated and control groups, and the consequences of using randomization, blocking before randomization, and matching after treatment assignment to achieve covariate balance. Applied researchers in a wide range of scien-tific disciplines seem to fall prey to one or more of these fallacies, and as a result make suboptimal design or analysis choices. To clarify these points, we derive a new four-part decomposition of the
Toward a Common Framework for Statistical Analysis and Development
- Journal of Computational and Graphical Statistics
, 2008
"... 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 ..."
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Cited by 48 (10 self)
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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.
Misunderstandings between experimentalists and observationalists about causal inference
- In the present volume
, 2010
"... Summary. We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fallacies of causal inference. These issues concern some of the most fundamental advantages and disadvantages of each basic research design. Problems include improper use of hypothesis tests for cov ..."
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Cited by 44 (6 self)
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Summary. We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fallacies of causal inference. These issues concern some of the most fundamental advantages and disadvantages of each basic research design. Problems include improper use of hypothesis tests for covariate balance between the treated and control groups, and the consequences of using randomization, blocking before randomization and matching after assignment of treatment to achieve covariate balance. Applied researchers in a wide range of scientific disciplines seem to fall prey to one or more of these fallacies and as a result make suboptimal design or analysis choices. To clarify these points, we derive a new four-part decomposition of the key estimation errors in making causal inferences. We then show how this decomposition can help scholars from different experimental and observational research traditions to understand better each other’s inferential problems and attempted solutions.
Multivariate Matching Methods That are Monotonic Imbalance Bounding ∗
, 2009
"... We introduce a new “Monotonic Imbalance Bounding ” (MIB) class of matching methods for causal inference that satisfies several important in-sample properties. MIB generalizes and extends in several new directions the only existing class, “Equal Percent Bias Reducing ” (EPBR), which is designed to sa ..."
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Cited by 42 (1 self)
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We introduce a new “Monotonic Imbalance Bounding ” (MIB) class of matching methods for causal inference that satisfies several important in-sample properties. MIB generalizes and extends in several new directions the only existing class, “Equal Percent Bias Reducing ” (EPBR), which is designed to satisfy weaker properties and only in expectation. We also offer strategies to obtain specific members of the MIB class, and present a member of this class, called Coarsened Exact Matching, whose properties we analyze from this new perspective. ∗Open source R and Stata software to implement the methods described herein (called CEM) is available at
Optimal full matching and related designs via network flows
- Journal of Computational and Graphical Statistics
, 2006
"... In the matched analysis of an observational study, confounding on covariates X is addressed by comparing members of a distinguished group (Z = 1) to controls (Z =0) only when they belong to the same matched set. The better matchings, therefore, are those whose matched sets exhibit both dispersion in ..."
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Cited by 30 (4 self)
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In the matched analysis of an observational study, confounding on covariates X is addressed by comparing members of a distinguished group (Z = 1) to controls (Z =0) only when they belong to the same matched set. The better matchings, therefore, are those whose matched sets exhibit both dispersion in Z and uniformity in X. For dispersion in Z, pair matching is best, creating matched sets that are equally balanced between the groups; but actual data place limits, often severe limits, on matched pairs’ uniformity in X. At the other extreme is full matching, the matched sets of which are as uniform in X as can be, while often so poorly dispersed in Z as to sacrifice efficiency. This article presents an algorithm for exploring the intermediate territory. Given requirements on matched sets ’ uniformity in X and dispersion in Z, the algorithm first decides the requirements ’ feasibility. In feasible cases, it furnishes a match that is optimal for X-uniformity among matches with Z-dispersion as stipulated. To illustrate, we describe the algorithm’s use in a study comparing womens ’ to mens ’ working conditions; and we compare our method to a commonly used alternative, greedy matching, which is neither optimal nor as flexible but is algorithmically much simpler. The comparison finds meaningful advantages, in terms of both bias and efficiency, for our more studied approach.
