Topic
Score
About: Score is a research topic. Over the lifetime, 1229 publications have been published within this topic receiving 27639 citations.
Papers published on a yearly basis
Papers
More filters
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.
Abstract: The propensity score plays a central role in a variety of causal inference settings. In particular, matching and weighting methods based on the estimated propensity score have become increasingly common in observational studies. Despite their popularity and theoretical appeal, the main practical difficulty of these methods is that the propensity score must be estimated. Researchers have found that slight misspecification of the propensity score model can result in substantial bias of estimated treatment effects. This workshop introduces a simple and yet powerful new methodology, covariate balancing propensity score (CBPS) estimation, which significantly improves the empirical performance of propensity score methods. The CBPS simultaneously optimizes the covariate balance and the prediction of treatment assignment by exploiting the dual characteristics of the propensity score as a covariate balancing score and the conditional probability of treatment assignment. The CBPS is shown to dramatically improve the poor empirical performance of propensity score matching and weighting methods reported in the literature. In addition, the CBPS can be extended to a number of other important settings, including the estimation of the generalized propensity score for non-binary treatments, the generalization of experimental estimates to a target population, and causal inference in the longitudinal settings with marginal structural models. The open-source R package, CBPS, is available for implementing the proposed methods.
963 citations
14 Jul 2005
TL;DR: The generalized propensity score (GPS) as discussed by the authors is a generalized version of the binary treatment propensity score, which was proposed to remove all biases associated with dierences in the covariates.
Abstract: of the binary treatment propensity score, which we label the generalized propensity score (GPS). We demonstrate that the GPS has many of the attractive properties of the binary treatment propensity score. Just as in the binary treatment case, adjusting for this scalar function of the covariates removes all biases associated with dierences in the covariates. The GPS also has certain balancing properties that can be used to assess the adequacy of particular specications of the score. We discuss estimation and inference in a parametric
963 citations
TL;DR: In this article, the authors introduce a class of robust estimators of the parameters of a stochastic utility function, called maximum score estimators, which require only weak distributional assumptions for consistency.
857 citations
TL;DR: In this article, linear rank statistics are developed for tests on regression coefficients with censored data, which arise as score statistics based on the marginal probability of a generalized rank vector, and the observed Fisher information provides a variance estimator generally, while in certain special cases a permutation approach to variance estimation is also possible.
Abstract: SUMMARY Linear rank statistics are developed for tests on regression coefficients with censored data These statistics arise as score statistics based on the marginal probability of a generalized rank vector The observed Fisher information provides a variance estimator generally, while in certain special cases a permutation approach to variance estimation is also possible The
708 citations
TL;DR: In this article, a decision theory formulation for population selection followed by estimating the mean of the selected population is presented, and the problem of finding the largest normal mean under Heteroscedasticity is addressed.
Abstract: 1 - Selection, Ranking, and Multiple Comparisons.- Sequential Selection Procedures for Multi-Factor Experiments Involving Koopman-Darmois Populations with Additivity.- Selection Problem for a Modified Multinomial (Voting) Model.- A Decision Theory Formulation for Population Selection Followed by Estimating the Mean of the Selected Population.- On the Problem of Finding the Largest Normal Mean under Heteroscedasticity.- On Least Favorable Configurations for Some Poisson Selection Rules and Some Conditional Tests.- Selection of the Best Normal Populations better Than a Control: Dependence Case.- Inference about the Change-Point in a Sequence of Random Variables: A Selection Approach.- On Confidence Sets in Multiple Comparisons.- 2 - Asymptoticand Sequential Analysis.- The VPRT: Optimal Sequential and Nonsequential Testing.- An Edgeworth Expansion for the Distribution of the F-Ratio under a Randomization Model for the Randomized Block Design.- On Bayes Sequential Tests.- Stochastic Search in a Square and on a Torus.- Distinguished Statistics, Loss of Information and a Theorem of Robert B. Davies.- Prophet Inequalities for Threshold Rules for Independent Bounded Random Variables.- Weak Convergence of the Aalen Estimator for a Censored Renewal Process.- Sequential Stein-Rule Maximum Likelihood Estimation: General Asymptotics.- Fixed Proportional Accuracy in Three Stages.- 3 - Estimationand Testing.- Dominating Inadmissible Tests in Exponential Family Models.- On Estimating Change Point in a Failure Rate.- A Nonparametric, Intersection-Union Test for Stochastic Order.- On Estimating the Number of Unseen Species and System Reliability.- The Effects of Variance Function Estimation on Prediction and Calibration: An Example.- On Estimating a Parameter and Its Score Function, II.- A Simple Test for the Equality of Correlation Matrices.- Conditions of Rao's Covariance Method Type for Set-Valued Estimators.- Conservation of Properties of Optimality of Some Statistical Tests and Point Estimators under Extensions of Distributions.- Some Recent Results in Signal Detection.- 4 - Design, and Comparisonof Experimentsand Distributions.- Comparison of Experiments and Information in Censored Data.- A Note on Approximate D-Optimal Designs for G x 2m.- Some Statistical Design Aspects of Estimating Automotive Emission Deterioration Factors.- Peakedness in Multivariate Distributions.- Spatial Designs.
572 citations