The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
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Derives contraction-based Q-value extensions for exponential utility and proves almost-sure convergence of two-timescale and one-timescale model-free algorithms in discounted MDPs.
Active inference framework for U-statistics using augmented IPW to optimize label queries and minimize variance under budget constraints.
citing papers explorer
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Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
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Reinforcement Learning for Exponential Utility: Algorithms and Convergence in Discounted MDPs
Derives contraction-based Q-value extensions for exponential utility and proves almost-sure convergence of two-timescale and one-timescale model-free algorithms in discounted MDPs.
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Learning U-Statistics with Active Inference
Active inference framework for U-statistics using augmented IPW to optimize label queries and minimize variance under budget constraints.