Dynamic thresholding designs identify a marginal policy effect via a tailored local linear regression estimator that generalizes the static regression discontinuity parameter.
Treatment Allocation under Uncertain Costs
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
abstract
We consider the problem of learning how to optimally allocate treatments whose cost is uncertain and can vary with pre-treatment covariates. This setting may arise in medicine if we need to prioritize access to a scarce resource that different patients would use for different amounts of time, or in marketing if we want to target discounts whose cost to the company depends on how much the discounts are used. Here, we show that the optimal treatment allocation rule under budget constraints is a thresholding rule based on priority scores (those with a higher score are treated first), and we propose a number of practical methods for learning these priority scores using data from a randomized trial. Our formal results leverage a statistical connection between our problem and that of learning heterogeneous treatment effects under endogeneity using an instrumental variable. We find our method to perform well in a number of empirical evaluations.
verdicts
UNVERDICTED 5representative citing papers
Game-theoretic modeling and difference-in-differences analysis using LLM releases show AI data center demand increases fossil generation, wholesale prices, and outages near data centers unless mitigated by behind-the-meter capacity.
The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
Optimal policies under budget and coverage constraints admit an affine threshold characterization with O(1) integrality gap in the LP relaxation; two algorithms (GLC and RC) are analyzed with performance guarantees that depend on cost homogeneity and constraint bindingness.
Develops optimal encouragement policies distinguishing responsiveness from efficacy, targeting induced take-up for fairness under budget constraints in non-adherence settings.
citing papers explorer
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Non-parametric Causal Inference in Dynamic Thresholding Designs
Dynamic thresholding designs identify a marginal policy effect via a tailored local linear regression estimator that generalizes the static regression discontinuity parameter.
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Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand
Game-theoretic modeling and difference-in-differences analysis using LLM releases show AI data center demand increases fossil generation, wholesale prices, and outages near data centers unless mitigated by behind-the-meter capacity.
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Set-Valued Policy Learning
The paper develops set-valued policies and conformal policy learning methods that output treatment sets with marginal coverage guarantees for robust decision-making under uncertainty.
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Optimal Policy Learning under Budget and Coverage Constraints
Optimal policies under budget and coverage constraints admit an affine threshold characterization with O(1) integrality gap in the LP relaxation; two algorithms (GLC and RC) are analyzed with performance guarantees that depend on cost homogeneity and constraint bindingness.
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Mind the Gap: Optimal and Equitable Encouragement Policies
Develops optimal encouragement policies distinguishing responsiveness from efficacy, targeting induced take-up for fairness under budget constraints in non-adherence settings.