Search Results for author:
Found 7 papers, 3 papers with code
Pushing the limits of fairness impossibility: Who's the fairest of them all?
The impossibility theorem of fairness is a foundational result in the algorithmic fairness literature.
Generalized Causal Tree for Uplift Modeling
In this paper, we propose a generalization of tree-based approaches to tackle multiple discrete and continuous-valued treatments.
Offline Reinforcement Learning for Mobile Notifications
Mobile notification systems have taken a major role in driving and maintaining user engagement for online platforms.
Optimal Convergence for Stochastic Optimization with Multiple Expectation Constraints
In this paper, we focus on the problem of stochastic optimization where the objective function can be written as an expectation function over a closed convex set.
Personalized Treatment Selection using Causal Heterogeneity
In this work, we develop a framework for personalization through (i) estimation of heterogeneous treatment effect at either a cohort or member-level, followed by (ii) selection of optimal treatment variants for cohorts (or members) obtained through (deterministic or stochastic) constrained optimization.
Inference for Individual Mediation Effects and Interventional Effects in Sparse High-Dimensional Causal Graphical Models
In particular, we assume that the causal structure of the treatment, the confounders, the potential mediators and the response is a (possibly unknown) directed acyclic graph (DAG).
