Journal ArticleDOI
The bias due to incomplete hatching
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TLDR
A practical example shows that the bias due to incomplete matching can be severe, and moreover, can be avoided entirely by using an appropriate multivariate nearest available matching algorithm, which, in the example, leaves only a small residual biasDue to inexact matching.Abstract:
Observational studies comparing groups of treated and control units are often used to estimate the effects caused by treatments. Matching is a method for sampling a large reservoir of potential controls to produce a control group of modest size that is ostensibly similar to the treated group. In practice, there is a trade-off between the desires to find matches for all treated units and to obtain matched treated-control pairs that are extremely similar to each other. We derive expressions for the bias in the average matched pair difference due to (1) the failure to match all treated units—incomplete matching, and (2) the failure to obtain exact matches—inexact matching. A practical example shows that the bias due to incomplete matching can be severe, and moreover, can be avoided entirely by using an appropriate multivariate nearest available matching algorithm, which in the example, leaves only a small residual bias due to inexact matching.read more
Citations
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Journal ArticleDOI
Statistics and Causal Inference
TL;DR: In this article, the authors use a particular model for causal inference (Holland and Rubin 1983; Rubin 1974) to critique the discussions of other writers on causation and causal inference.
Journal ArticleDOI
Matching Methods for Causal Inference: A Review and a Look Forward
TL;DR: A structure for thinking about matching methods and guidance on their use is provided, 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.
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.
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.
Book ChapterDOI
Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates
Donald B. Rubin,Neal Thomas +1 more
TL;DR: Propensity score matching as mentioned in this paper is a class of multivariate methods used in comparative studies to construct treated and matched control samples that have similar distributions on many covariates, both observed and unobserved.
References
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Journal ArticleDOI
The central role of the propensity score in observational studies for causal effects
TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
Journal ArticleDOI
Estimating causal effects of treatments in randomized and nonrandomized studies.
TL;DR: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented in this paper, where the objective is to specify the benefits of randomization in estimating causal effects of treatments.
Journal ArticleDOI
Bayesian Inference for Causal Effects: The Role of Randomization
TL;DR: In this article, the authors make clear the role of mechanisms that sample experimental units, assign treatments and record data, and that unless these mechanisms are ignorable, the Bayesian must model them in the data analysis and confront inferences for causal effects that are sensitive to the specification of the prior distribution of the data.