Full matching in an observational study of coaching for the SAT
TLDR
In this paper, the authors evaluate the performance of full matching for the first time, modifying it in order to minimize variance as well as bias and then using it to compare coached and uncoached takers of the SAT.Abstract:
Among matching techniques for observational studies, full matching is in principle the best, in the sense that its alignment of comparable treated and control subjects is as good as that of any alternate method, and potentially much better. This article evaluates the practical performance of full matching for the first time, modifying it in order to minimize variance as well as bias and then using it to compare coached and uncoached takers of the SAT. In this new version, with restrictions on the ratio of treated subjects to controls within matched sets, full matching makes use of many more observations than does pair matching, but achieves far closer matches than does matching with k≥ 2 controls. Prior to matching, the coached and uncoached groups are separated on the propensity score by 1.1 SDs. Full matching reduces this separation to 1% or 2% of an SD. In older literature comparing matching and regression, Cochran expressed doubts that any method of adjustment could substantially reduce observed bias ...read more
Citations
More filters
Journal ArticleDOI
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
TL;DR: The propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects, and different causal average treatment effects and their relationship with propensity score analyses are described.
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
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
TL;DR: 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.
Book
Counterfactuals and Causal Inference: Methods and Principles for Social Research
TL;DR: In this article, the authors proposed a method to estimate causal effects by conditioning on observed variables to block backdoor paths in observational social science research, but the method is limited to the case of causal exposure and identification criteria for conditioning estimators.
References
More filters
Journal ArticleDOI
Categorical Data Analysis
TL;DR: In this article, categorical data analysis was used for categorical classification of categorical categorical datasets.Categorical Data Analysis, categorical Data analysis, CDA, CPDA, CDSA
Book
Experimental and Quasi-Experimental Designs for Research
TL;DR: A survey drawn from social science research which deals with correlational, ex post facto, true experimental, and quasi-experimental designs and makes methodological recommendations is presented in this article.
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
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
TL;DR: In this article, five subclasses defined by the estimated propensity score are constructed that balance 74 covariates, and thereby provide estimates of treatment effects using direct adjustment, and these subclasses are applied within sub-populations, and model-based adjustments are then used to provide estimates for treatment effects within these sub-population.
Journal ArticleDOI
Chi-Square Tests with One Degree of Freedom; Extensions of the Mantel-Haenszel Procedure
TL;DR: In this article, a method for analyzing multiple 2×2 contingency tables arising in retrospective studies of disease is extended in application and form, which includes comparisons of age-adjusted death rates, life-table analyses, comparisons of two sets of quantal dosage response data, and miscellaneous laboratory applications as appropriate.