no code implementations • 2 Oct 2023 • Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, Daniel E. Ho
Unlike similar existing approaches, our methods take advantage of contextual information -- specifically, the relationships between a model's predictions and the probabilistic prediction of protected attributes, given the true protected attribute, and vice versa -- to provide tighter bounds on the true disparity.
1 code implementation • 28 Jul 2023 • Patrick Vossler, Sina Aghaei, Nathan Justin, Nathanael Jo, Andrés Gómez, Phebe Vayanos
ODTLearn is an open-source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the mixed-integer optimization (MIO) framework proposed in Aghaei et al. (2019) and several of its extensions.
no code implementations • 6 Jun 2023 • Caroline M. Johnston, Patrick Vossler, Simon Blessenohl, Phebe Vayanos
Preference elicitation leverages AI or optimization to learn stakeholder preferences in settings ranging from marketing to public policy.
no code implementations • 2 Dec 2021 • Xinze Du, Yingying Fan, Jinchi Lv, Tianshu Sun, Patrick Vossler
Under some regularity conditions, the observed response can be formulated as the response of a mean regression problem with both the confounding variables and the treatment indicator as the independent variables.
no code implementations • 25 Aug 2018 • Emre Demirkaya, Yingying Fan, Lan Gao, Jinchi Lv, Patrick Vossler, Jingbo Wang
The weighted nearest neighbors (WNN) estimator has been popularly used as a flexible and easy-to-implement nonparametric tool for mean regression estimation.