arXiv Statistics ML
Doubly-robust causal inference for multi-valued treatment regimes with recurrent neural networks
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MedGym:A Unified Continuous-Time Benchmark for Dynamic Medical Treatment Reinforcement Learning
arXiv:2606. 01028v1 Announce Type: new Abstract: Medical treatment recommendation poses several challenges to reinforcement learning (RL): patient physiology evolves in continuous time, measurements and interventions are performed at irregular intervals, and treatment effects vary substantially across individuals.
arXiv Machine Learning
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A Unified Benchmark for Dynamic Medical Treatment Reinforcement Learning
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Causal Foundation Models with Continuous Treatments
arXiv:2605. 15133v2 Announce Type: replace Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines.
arXiv Statistics ML
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Identifying Causal Effects Using a Single Proxy Variable
The paper tackles the problem of estimating causal effects when an unobserved confounder is present. It assumes a single, possibly multi‑dimensional proxy variable for the confounder and knowledge of the mechanism that generates this proxy. Under the Single Proxy Identifiability of Causal Effects (SPICE) assumption, the authors prove that the error mechanism is complete and causal effects are identifiable, extending prior proxy‑based results to continuous, multi‑dimensional settings and more flexible functional forms. They also introduce SPICE‑Net, a neural‑network‑based framework for estimating causal effects applicable to both discrete and continuous treatments.
arXiv Machine Learning
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TLRNet: Estimating Individual Treatment Effect based on Local Information and Single Learner Structure
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arXiv Machine Learning
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Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time
arXiv:2604.26070v3 Announce Type: replace Abstract: Causal inference in continuous-time sequential decision problems is challenged by hidden confounding and partially observed states. We show that, u...