no code implementations • 28 May 2024 • Christoph Kern, Michael Kim, Angela Zhou
We show improvements in bias and mean squared error in simulations with increasingly larger covariate shift, and on a semi-synthetic case study of a parallel large observational study and smaller randomized controlled experiment.
no code implementations • 29 Apr 2024 • Ezinne Nwankwo, Michael I. Jordan, Angela Zhou
However, if causal effects are heterogeneous and predictable from covariates, personalized treatment decisions can improve individual outcomes and contribute to both efficiency and equity.
no code implementations • 23 Jan 2024 • Angela Zhou
This paper studies offline reinforcement learning with linear function approximation in a setting with decision-theoretic, but not estimation sparsity.
no code implementations • 1 Feb 2023 • David Bruns-Smith, Angela Zhou
Offline reinforcement learning is important in domains such as medicine, economics, and e-commerce where online experimentation is costly, dangerous or unethical, and where the true model is unknown.
1 code implementation • 9 Nov 2022 • Connor Lawless, Angela Zhou
In this short technical note we propose a baseline for decision-aware learning for contextual linear optimization, which solves stochastic linear optimization when cost coefficients can be predicted based on context information.
no code implementations • 29 Aug 2022 • Michael I. Jordan, Yixin Wang, Angela Zhou
We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing, with a focus on functionals that arise in causal inference.
no code implementations • 25 Feb 2022 • Wenshuo Guo, Michael I. Jordan, Angela Zhou
Under this framework, a decision-maker's utility depends on the policy-dependent optimization, which introduces a fundamental challenge of \textit{optimization} bias even for the case of policy evaluation.
no code implementations • 19 Oct 2021 • Nathan Kallus, Angela Zhou
We study off-policy evaluation and learning from sequential data in a structured class of Markov decision processes that arise from repeated interactions with an exogenous sequence of arrivals with contexts, which generate unknown individual-level responses to agent actions.
no code implementations • 21 Dec 2020 • Nathan Kallus, Angela Zhou
These different application areas may lead to different concerns around fairness, welfare, and equity on different objectives: price burdens on consumers, price envy, firm revenue, access to a good, equal access, and distributional consequences when the good in question further impacts downstream outcomes of interest.
no code implementations • NeurIPS 2020 • Nathan Kallus, Angela Zhou
We develop a robust approach that estimates sharp bounds on the (unidentifiable) value of a given policy in an infinite-horizon problem given data from another policy with unobserved confounding, subject to a sensitivity model.
1 code implementation • NeurIPS 2019 • Nathan Kallus, Angela Zhou
Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit.
1 code implementation • 4 Jun 2019 • Nathan Kallus, Angela Zhou
Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit.
1 code implementation • 1 Jun 2019 • Nathan Kallus, Xiaojie Mao, Angela Zhou
In this paper we study a fundamental challenge to assessing disparate impacts in practice: protected class membership is often not observed in the data.
1 code implementation • NeurIPS 2019 • Nathan Kallus, Angela Zhou
To better account for this, in this paper, we investigate the fairness of predictive risk scores from the point of view of a bipartite ranking task, where one seeks to rank positive examples higher than negative ones.
no code implementations • 5 Oct 2018 • Nathan Kallus, Xiaojie Mao, Angela Zhou
We study the problem of learning conditional average treatment effects (CATE) from observational data with unobserved confounders.
no code implementations • ICML 2018 • Nathan Kallus, Angela Zhou
We connect these lines of work and study the residual unfairness that arises when a fairness-adjusted predictor is not actually fair on the target population due to systematic censoring of training data by existing biased policies.
no code implementations • NeurIPS 2018 • Nathan Kallus, Angela Zhou
We study the problem of learning personalized decision policies from observational data while accounting for possible unobserved confounding.
no code implementations • 16 Feb 2018 • Nathan Kallus, Angela Zhou
We study the problem of policy evaluation and learning from batched contextual bandit data when treatments are continuous, going beyond previous work on discrete treatments.
no code implementations • 30 Jan 2017 • Angela Zhou, Irineo Cabreros, Karan Singh
We consider the problem of optimal budget allocation for crowdsourcing problems, allocating users to tasks to maximize our final confidence in the crowdsourced answers.