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Found 8 papers, 2 papers with code
Interpretable Personalized Experimentation
Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments.
Online Discrepancy Minimization via Persistent Self-Balancing Walks
We study the online discrepancy minimization problem for vectors in $\mathbb{R}^d$ in the oblivious setting where an adversary is allowed fix the vectors $x_1, x_2, \ldots, x_n$ in arbitrary order ahead of time.
Data Structures and Algorithms Discrete Mathematics Combinatorics
Experimentation for Homogenous Policy Change
When the Stable Unit Treatment Value Assumption (SUTVA) is violated and there is interference among units, there is not a uniquely defined Average Treatment Effect (ATE), and alternative estimands may be of interest, among them average unit-level differences in outcomes under different homogeneous treatment policies.
Methodology Applications
Efficient Balanced Treatment Assignments for Experimentation
In this work, we reframe the problem of balanced treatment assignment as optimization of a two-sample test between test and control units.
Designing Transportable Experiments
To reduce the variance of our estimator, we design a covariate balance condition (Target Balance) between the treatment and control groups based on the target population.
Methodology
Real-world Video Adaptation with Reinforcement Learning
Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE).
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints
Real-world applications frequently have constraints with respect to a currently deployed policy.
Balanced off-policy evaluation in general action spaces
We show that minimizing the risk of the classifier implies minimization of imbalance to the desired counterfactual distribution of state-action pairs.
