K
Kari Lock Morgan
Researcher at Pennsylvania State University
Publications - 18
Citations - 1248
Kari Lock Morgan is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Covariate & Nonparametric statistics. The author has an hindex of 8, co-authored 15 publications receiving 778 citations. Previous affiliations of Kari Lock Morgan include Duke University & Harvard University.
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Balancing Covariates via Propensity Score Weighting
TL;DR: In this article, a general class of weights, called balancing weights, is defined to balance the weighted distributions of the covariates between treatment groups, and a new weighting scheme, the overlap weights, are proposed to minimize the variance of the weighted average treatment effect among the class of balancing weights.
Journal ArticleDOI
Rerandomization to improve covariate balance in experiments
Kari Lock Morgan,Donald B. Rubin +1 more
TL;DR: In this article, the authors show that covariate data are available before units are exposed to treatments and can be used to check covariate balance before the physical experiment takes place, provided a precise definition of imbalance has been specified.
Journal ArticleDOI
Balancing Covariates via Propensity Score Weighting
TL;DR: In this paper, a general class of weighting strategies for balancing covariates is proposed, which unifies existing weighting methods, including commonly used weights such as inverse probability weights as special cases.
Journal ArticleDOI
Rerandomization to improve covariate balance in experiments
Kari Lock Morgan,Donald B. Rubin +1 more
TL;DR: In this paper, the authors show that if covariate data are available before units are exposed to treatments, these chance imbalances can be mitigated by first checking covariate balance before the physical experiment takes place.
Journal ArticleDOI
Rerandomization to Balance Tiers of Covariates
Kari Lock Morgan,Donald B. Rubin +1 more
TL;DR: This work illustrates how rerandomization could have improved the design of an already conducted randomized experiment on vocabulary and mathematics training programs, then provides a re randomization procedure for covariates that vary in importance, and offers other extensions for re Randomization, including methods addressing computational efficiency.