Health Systems, Economic Evaluations, Quality of Life

Últimos artículos

• Latent Utility Q-Learning for Preference-Adaptive Dynamic Treatment Regimes

  Joshua P. Zitovsky, Yating Zou, Leslie Wilson, Michael R. Kosorok · 7 de agosto de 2026

  Optimizing individualized treatment sequences for patients who weigh multiple, competing outcomes differently poses a challenge for dynamic treatment regime (DTR) methods, which typically assume a single univariate outcome. We propose Latent Utility Q-Learning (LUQ-Learning), which estimates DTRs op…

• A Systematic Literature Review on the NIS2 Directive

  Jukka Ruohonen · 24 de junio de 2026

  The second network and information security (NIS2) directive was enacted in the European Union (EU) in late 2022. It deals particularly with European critical infrastructures, enlarging their scope substantially from an older directive that only considered the energy and transport sectors as critica…

• Quantifying Harm

  Sander Beckers, Hana Chockler, Joseph Y. Halpern · 7 de mayo de 2026

  In earlier work we defined a qualitative notion of harm: either harm is caused, or it is not. For practical applications, we often need to quantify harm; for example, we may want to choose the least harmful of a set of possible interventions. In this work, which is an expanded version of an earlier …

• Estimating Joint Interventional Distributions from Marginal Interventional Data

  Sergio Hernan Garrido Mejia, Elke Kirschbaum, Armin Keki\'c, Bernhard Sch\"olkopf, Atalanti Mastakouri · 20 de abril de 2026

  In this paper we show how to exploit interventional data to acquire the joint conditional distribution of all the variables using the Maximum Entropy principle. To this end, we extend the Causal Maximum Entropy method to make use of interventional data in addition to observational data. Using Lagran…

• Data-Driven Influence Functions for Optimization-Based Causal Inference

  Michael I. Jordan, Yixin Wang, Angela Zhou · 16 de marzo de 2026

  We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing, with a focus on functionals that arise in causal inference. We study the case where probability distributions are not known a priori but need to be estimated from data. These e…

• From Bench to Bedside: A Review of Clinical Trials in Drug Discovery and Development

  Tianyang Wang, Ming Liu, Benji Peng, Xinyuan Song, Charles Zhang, Xintian Sun, Qian Niu, Junyu Liu, Silin Chen, Keyu Chen, Ming Li, Pohsun Feng, Ziqian Bi, Yunze Wang, Yichao Zhang, Cheng Fei, Lawrence KQ Yan, Ziyuan Qin, Riyang Bao, Zekun Jiang · 6 de enero de 2026

  Clinical trials are an indispensable part of the drug development process, bridging the gap between basic research and clinical application. During the development of new drugs, clinical trials are used not only to evaluate the safety and efficacy of the drug but also to explore its dosage, treatmen…

• Unraveling the Rainbow: can value-based methods schedule?

  Arthur Corr\^ea, Alexandre Jesus, Paulo Nascimento, Crist\'ov\~ao Silva, Samuel Moniz · 1 de diciembre de 2025

  In this work, we conduct an extensive empirical study of several deep reinforcement learning algorithms on two challenging combinatorial optimization problems: the job-shop and flexible job-shop scheduling problems, both fundamental challenges with multiple industrial applications. Broadly, deep rei…

• Variance-Bounded Evaluation of Entity-Centric AI Systems Without Ground Truth: Theory and Measurement

  Kaihua Ding · 5 de noviembre de 2025

  Reliable evaluation of AI systems remains a fundamental challenge when ground truth labels are unavailable, particularly for systems generating natural language outputs like AI chat and agent systems. Many of these AI agents and systems focus on entity-centric tasks. In enterprise contexts, organiza…