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pro vyhledávání: '"Lee, Jaron"'
Autor:
Phung, Trung, Lee, Jaron J. R., Oladapo-Shittu, Opeyemi, Klein, Eili Y., Gurses, Ayse Pinar, Hannum, Susan M., Weems, Kimberly, Marsteller, Jill A., Cosgrove, Sara E., Keller, Sara C., Shpitser, Ilya
A common type of zero-inflated data has certain true values incorrectly replaced by zeros due to data recording conventions (rare outcomes assumed to be absent) or details of data recording equipment (e.g. artificial zeros in gene expression data). E
Externí odkaz:
http://arxiv.org/abs/2406.00549
Causal inference is made challenging by confounding, selection bias, and other complications. A common approach to addressing these difficulties is the inclusion of auxiliary data on the superpopulation of interest. Such data may measure a different
Externí odkaz:
http://arxiv.org/abs/2404.06602
Quantitative methods in Human-Robot Interaction (HRI) research have primarily relied upon randomized, controlled experiments in laboratory settings. However, such experiments are not always feasible when external validity, ethical constraints, and ea
Externí odkaz:
http://arxiv.org/abs/2310.20468
We implement Ananke: an object-oriented Python package for causal inference with graphical models. At the top of our inheritance structure is an easily extensible Graph class that provides an interface to several broadly useful graph-based algorithms
Externí odkaz:
http://arxiv.org/abs/2301.11477
Off-policy evaluation methods are important in recommendation systems and search engines, where data collected under an existing logging policy is used to estimate the performance of a new proposed policy. A common approach to this problem is weighti
Externí odkaz:
http://arxiv.org/abs/2203.02807
Autor:
Lee, Jaron J. R., Mallett, Agatha S., Shpitser, Ilya, Campbell, Aimee, Nunes, Edward, Scharfstein, Daniel O.
Scharfstein et al. (2021) developed a sensitivity analysis model for analyzing randomized trials with repeatedly measured binary outcomes that are subject to nonmonotone missingness. Their approach becomes computationally intractable when the number
Externí odkaz:
http://arxiv.org/abs/2105.08868
Causal analyses of longitudinal data generally assume that the qualitative causal structure relating variables remains invariant over time. In structured systems that transition between qualitatively different states in discrete time steps, such an a
Externí odkaz:
http://arxiv.org/abs/2008.10706
Black box models in machine learning have demonstrated excellent predictive performance in complex problems and high-dimensional settings. However, their lack of transparency and interpretability restrict the applicability of such models in critical
Externí odkaz:
http://arxiv.org/abs/2006.04732
Autor:
Lee, Jaron J. R., Shpitser, Ilya
Causal inference quantifies cause-effect relationships by estimating counterfactual parameters from data. This entails using \emph{identification theory} to establish a link between counterfactual parameters of interest and distributions from which d
Externí odkaz:
http://arxiv.org/abs/2004.01157
Autor:
Lee, Jaron J.R., Srinivasan, Ranjani, Ong, Chin Siang, Alejo, Diane, Schena, Stefano, Shpitser, Ilya, Sussman, Marc, Whitman, Glenn J.R., Malinsky, Daniel
Publikováno v:
In The Journal of Thoracic and Cardiovascular Surgery November 2023 166(5):e446-e462
