Roee Gutman

TL;DR: A computational algorithm, clustering by inferred models of evolution (CLIME), which inputs a eukaryotic species tree, homology matrix, and pathway (gene set) of interest, and reveals unanticipated evolutionary modularity and coevolving components.

TL;DR: The propensity score is a common tool for estimating the causal effect of a binary treatment in observational data as discussed by the authors, which can reduce the initial covariate bias between the treatment and control groups.

TL;DR: The procedure generates m datasets in which the matches between the two files are imputed and results can be combined using Rubin's multiple imputation rules, and can be applied in other file-linking applications.

Abstract: The propensity score is a common tool for estimating the causal effect of a binary treatment in observational data. In this setting, matching, subclassification, imputation or inverse probability weighting on the propensity score can reduce the initial covariate bias between the treatment and control groups. With more than two treatment options, however, estimation of causal effects requires additional assumptions and techniques, the implementations of which have varied across disciplines. This paper reviews current methods, and it identifies and contrasts the treatment effects that each one estimates. Additionally, we propose possible matching techniques for use with multiple, nominal categorical treatments, and use simulations to show how such algorithms can yield improved covariate similarity between those in the matched sets, relative the pre-matched cohort. To sum, this manuscript provides a synopsis of how to notate and use causal methods for categorical treatments.

TL;DR: This work shows that the new 'multiple-imputation using two subclassification splines' method appears to be the most efficient and has coverage levels that are closest to nominal, and can estimate finite population average causal effects as well as non-linear causal estimands.