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Multivariate Matching Methods That are Monotonic Imbalance Bounding
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In this paper, the authors introduce a new ''Monotonic imbalance bounding'' (MIB) class of matching methods for causal inference that satisfies several important in-sample properties.Abstract:
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.read more
Citations
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Journal ArticleDOI
Causal Inference without Balance Checking: Coarsened Exact Matching
TL;DR: It is shown that CEM possesses a wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use.
Journal ArticleDOI
Why Propensity Scores Should Not Be Used for Matching
Gary King,Richard A. Nielsen +1 more
TL;DR: It is shown that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal—thus increasing imbalance, inefficiency, model dependence, and bias.
Journal ArticleDOI
Covariate balancing propensity score.
Kosuke Imai,Marc Ratkovic +1 more
TL;DR: Covariate balancing propensity score (CBPS) as mentioned in this paper was proposed to improve the empirical performance of propensity score matching and weighting methods by exploiting the dual characteristics of the propensity score as a covariate balancing score and the conditional probability of treatment assignment.
Journal ArticleDOI
Independent Boards and Innovation
TL;DR: In this article, the authors show that firms that transition to independent boards focus on more crowded and familiar areas of technology, and that the citation increase comes mainly from incremental patents in the middle of the citation distribution; the numbers of uncited and highly cited patents do not change significantly.
Journal ArticleDOI
cem: Software for Coarsened Exact Matching
TL;DR: The program implements the coarsened exact matching (CEM) algorithm, described below, which may be used alone or in combination with any existing matching method.
References
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Journal ArticleDOI
Inference and missing data
TL;DR: In this article, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.
Journal ArticleDOI
Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score
TL;DR: This article used multivariate matching methods in an observational study of the effects of prenatal exposure to barbiturates on subsequent psychological development, using the propensity score as a distinct matching variable.
Journal ArticleDOI
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
TL;DR: A unified approach is proposed that makes it possible for researchers to preprocess data with matching and then to apply the best parametric techniques they would have used anyway and this procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences.
Posted Content
Evaluating the Econometric Evaluations of Training Programs with Experimental Data
TL;DR: The National Supported Work Program employed an experimental design that randomly assigned some participants into a treatment group, receiving training, and the rest into a control group receiving no training as mentioned in this paper, and the difference between the post-training earnings of the two groups provided an unbiased estimate of the impact of the program.
Book ChapterDOI
Matching using estimated propensity scores: relating theory to practice.
Donald B. Rubin,Neal Thomas +1 more
TL;DR: These results delineate the wide range of settings in which matching on estimated linear propensity scores performs well, thereby providing useful information for the design of matching studies and applying theoretical approximations to practice.