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pro vyhledávání: '"Hejazi, Nima S"'
Constrained learning has become increasingly important, especially in the realm of algorithmic fairness and machine learning. In these settings, predictive models are developed specifically to satisfy pre-defined notions of fairness. Here, we study t
Externí odkaz:
http://arxiv.org/abs/2404.09847
Heterogeneous treatment effects are driven by treatment effect modifiers, pre-treatment covariates that modify the effect of a treatment on an outcome. Current approaches for uncovering these variables are limited to low-dimensional data, data with w
Externí odkaz:
http://arxiv.org/abs/2304.05323
Autor:
Hejazi, Nima S., van der Laan, Mark J.
Publikováno v:
Observational Studies, 2023
About forty years ago, in a now--seminal contribution, Rosenbaum & Rubin (1983) introduced a critical characterization of the propensity score as a central quantity for drawing causal inferences in observational study settings. In the decades since,
Externí odkaz:
http://arxiv.org/abs/2208.08065
Continuous treatments have posed a significant challenge for causal inference, both in the formulation and identification of scientifically meaningful effects and in their robust estimation. Traditionally, focus has been placed on techniques applicab
Externí odkaz:
http://arxiv.org/abs/2205.05777
Autor:
Juraska, Michal *, Early, Angela M, Li, Li, Schaffner, Stephen F, Lievens, Marc, Khorgade, Akanksha, Simpkins, Brian, Hejazi, Nima S, Benkeser, David, Wang, Qi, Mercer, Laina D, Adjei, Samuel, Agbenyega, Tsiri, Anderson, Scott, Ansong, Daniel, Bii, Dennis K, Buabeng, Patrick B Y, English, Sean, Fitzgerald, Nicholas, Grimsby, Jonna, Kariuki, Simon K, Otieno, Kephas, Roman, François, Samuels, Aaron M, Westercamp, Nelli, Ockenhouse, Christian F, Ofori-Anyinam, Opokua, Lee, Cynthia K, MacInnis, Bronwyn L, Wirth, Dyann F, Gilbert, Peter B, Neafsey, Daniel E **
Publikováno v:
In The Lancet Infectious Diseases September 2024 24(9):1025-1036
Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for lo
Externí odkaz:
http://arxiv.org/abs/2202.03513
We present an end-to-end methodological framework for causal segment discovery that aims to uncover differential impacts of treatments across subgroups of users in large-scale digital experiments. Building on recent developments in causal inference a
Externí odkaz:
http://arxiv.org/abs/2111.01223
Autor:
Li, Haodong, Rosete, Sonali, Coyle, Jeremy, Phillips, Rachael V., Hejazi, Nima S., Malenica, Ivana, Arnold, Benjamin F., Benjamin-Chung, Jade, Mertens, Andrew, Colford Jr, John M., van der Laan, Mark J., Hubbard, Alan E.
Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targe
Externí odkaz:
http://arxiv.org/abs/2109.14048
Autor:
Gilbert, Peter B., Fong, Youyi, Hejazi, Nima S., Kenny, Avi, Huang, Ying, Carone, Marco, Benkeser, David, Follmann, Dean
Publikováno v:
In Vaccine 2 April 2024 42(9):2181-2190
The covariance matrix plays a fundamental role in many modern exploratory and inferential statistical procedures, including dimensionality reduction, hypothesis testing, and regression. In low-dimensional regimes, where the number of observations far
Externí odkaz:
http://arxiv.org/abs/2102.09715