S
Stefano Maria Iacus
Researcher at University of Milan
Publications - 216
Citations - 21027
Stefano Maria Iacus is an academic researcher from University of Milan. The author has contributed to research in topics: Estimator & Monte Carlo method. The author has an hindex of 31, co-authored 206 publications receiving 18802 citations. Previous affiliations of Stefano Maria Iacus include Ulster University.
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Bioconductor: open software development for computational biology and bioinformatics
Robert Gentleman,Vincent J. Carey,Douglas M. Bates,Benjamin M. Bolstad,Marcel Dettling,Sandrine Dudoit,Byron Ellis,Laurent Gautier,Yongchao Ge,Jeff Gentry,Kurt Hornik,Torsten Hothorn,Wolfgang Huber,Stefano Maria Iacus,Rafael A. Irizarry,Friedrich Leisch,Cheng Li,Martin Maechler,A. J. Rossini,Günther Sawitzki,Colin A. Smith,Gordon K. Smyth,Luke Tierney,Jean Yang,Jianhua Zhang +24 more
TL;DR: Details of the aims and methods of Bioconductor, the collaborative creation of extensible software for computational biology and bioinformatics, and current challenges are described.
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Causal Inference without Balance Checking: Coarsened Exact Matching
TL;DR: It is shown that CEM possesses a wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use.
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cem: Coarsened exact matching in Stata
TL;DR: A Stata implementation of coarsened exact matching, a new method for improving the estimation of causal effects by reducing imbalance in covariates between treated and control groups, is introduced.
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Multivariate Matching Methods That are Monotonic Imbalance Bounding
TL;DR: In this paper, the authors introduce a new class of matching methods called Monotonic Imbalance Bounding (MIB), which generalizes and extends EPBR in several new directions.
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Multivariate Matching Methods That are Monotonic Imbalance Bounding
TL;DR: In this paper, the authors introduce a new ''Monotonic imbalance bounding'' (MIB) class of matching methods for causal inference that satisfies several important in-sample properties.