7 papers · 1 filter
Contextual Linear Optimization with Partial Feedback
Yichun Hu, Nathan Kallus, Xiaojie Mao +1
Contextual linear optimization (CLO) uses predictive contextual features to reduce uncertainty in random cost coefficients in the objective and thereby improve decision-making perf…
Robust and Agnostic Learning of Conditional Distributional Treatment Effects
Nathan Kallus, Miruna Oprescu
The conditional average treatment effect (CATE) is the best measure of individual causal effects given baseline covariates. However, the CATE only captures the (conditional) averag…
Variation Due to Regularization Tractably Recovers Bayesian Deep Learning
James McInerney, Nathan Kallus
Uncertainty quantification in deep learning is crucial for safe and reliable decision-making in downstream tasks. Existing methods quantify uncertainty at the last layer or other a…
The Central Role of the Loss Function in Reinforcement Learning
Kaiwen Wang, Nathan Kallus, Wen Sun
This paper illustrates the central role of loss functions in data-driven decision making, providing a comprehensive survey on their influence in cost-sensitive classification (CSC)…
SNPL: Simultaneous Policy Learning and Evaluation for Safe Multi-Objective Policy Improvement
Brian Cho, Ana-Roxana Pop, Ariel Evnine +1
To design effective digital interventions, experimenters face the challenge of learning decision policies that balance multiple objectives using offline data. Often, they aim to de…
On the role of surrogates in the efficient estimation of treatment effects with limited outcome data
Nathan Kallus, Xiaojie Mao
In many experimental and observational studies, the outcome of interest is often difficult or expensive to observe, reducing effective sample sizes for estimating average treatment…