:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Kubota, Kohsuke, Takahashi, Mitsuhiro, Saito, Yuta
Format:	Preprint
Published:	2026
Subjects:	Methodology Artificial Intelligence Machine Learning
Online Access:	https://arxiv.org/abs/2603.22900
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Censoring-Aware Tree-Based Reinforcement Learning for Estimating Dynamic Treatment Regimes with Censored Outcomes
by: Paul, Animesh Kumar, et al.
Published: (2025)

Causal Inference under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatment Effects
by: Kubota, Kohsuke, et al.
Published: (2025)

Multiple Treatments Causal Effects Estimation with Task Embeddings and Balanced Representation Learning
by: Murakami, Yuki, et al.
Published: (2025)

Off-Policy Evaluation and Learning for the Future under Non-Stationarity
by: Shimizu, Tatsuhiro, et al.
Published: (2025)

Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
by: Shimizu, Tatsuhiro, et al.
Published: (2024)

ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods
by: De Moor, Jakob, et al.
Published: (2025)

SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation
by: Kiyohara, Haruka, et al.
Published: (2023)

Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation
by: Kiyohara, Haruka, et al.
Published: (2023)

Adaptive Experimental Design for Policy Learning
by: Kato, Masahiro, et al.
Published: (2024)

Estimating Heterogeneous Treatment Effects on Survival Outcomes Using Counterfactual Censoring Unbiased Transformations
by: Xu, Shenbo, et al.
Published: (2024)

Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints
by: Das, Udvas, et al.
Published: (2024)

Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
by: Petrungaro, Bruno, et al.
Published: (2026)

Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimation
by: Li, He, et al.
Published: (2025)

Overcoming Dependent Censoring in the Evaluation of Survival Models
by: Lillelund, Christian Marius, et al.
Published: (2025)

CausalGuard: Conformal Inference under Graph Uncertainty
by: Singh, Vikash, et al.
Published: (2026)

Information-Theoretic Causal Bounds under Unmeasured Confounding
by: Jung, Yonghan, et al.
Published: (2026)

Effective Causal Discovery under Identifiable Heteroscedastic Noise Model
by: Yin, Naiyu, et al.
Published: (2023)

Evaluating the Effectiveness of Index-Based Treatment Allocation
by: Boehmer, Niclas, et al.
Published: (2024)

Doubly Robust Conformalized Survival Analysis with Right-Censored Data
by: Sesia, Matteo, et al.
Published: (2024)

Causal Discovery from Heteroscedastic Stochastic Dynamical Systems under Imperfect Physical Models
by: Chen, Jianhong, et al.
Published: (2026)

A Causal Framework for Evaluating ICU Discharge Strategies
by: Simha, Sagar Nagaraj, et al.
Published: (2026)

Weak Supervision Performance Evaluation via Partial Identification
by: Polo, Felipe Maia, et al.
Published: (2023)

A Consequentialist Critique of Binary Classification Evaluation: Theory, Practice, and Tools
by: Flores, Gerardo, et al.
Published: (2025)

CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios
by: Yi, Huiyang, et al.
Published: (2026)

Deep Partially Linear Transformation Model for Right-Censored Survival Data
by: Yin, Junkai, et al.
Published: (2024)

Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation
by: Martinon, Grégoire, et al.
Published: (2026)

An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes
by: Plecko, Drago, et al.
Published: (2025)

Evaluation of Stress Detection as Time Series Events -- A Novel Window-Based F1-Metric
by: Skat-Rørdam, Harald Vilhelm, et al.
Published: (2025)

Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning
by: Zhang, Jiaru, et al.
Published: (2025)

A Two-Stage Feature Selection Approach for Robust Evaluation of Treatment Effects in High-Dimensional Observational Data
by: Islam, Md Saiful, et al.
Published: (2021)

Causal Temporal Regime Structure Learning
by: Rahmani, Abdellah, et al.
Published: (2023)

Distribution Matching for Self-Supervised Transfer Learning
by: Jiao, Yuling, et al.
Published: (2025)

Measure-Theoretic Anti-Causal Representation Learning
by: Behnam, Arman, et al.
Published: (2025)

Causal State Distillation for Explainable Reinforcement Learning
by: Lu, Wenhao, et al.
Published: (2023)

DIGIC: Domain Generalizable Imitation Learning by Causal Discovery
by: Chen, Yang, et al.
Published: (2024)

Learning Causal Abstractions of Linear Structural Causal Models
by: Massidda, Riccardo, et al.
Published: (2024)

Learning DAGs from Data with Few Root Causes
by: Misiakos, Panagiotis, et al.
Published: (2023)

Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
by: Auddy, Arnab, et al.
Published: (2026)

Towards Learning and Explaining Indirect Causal Effects in Neural Networks
by: Reddy, Abbavaram Gowtham, et al.
Published: (2023)

Self-Labeling in Multivariate Causality and Quantification for Adaptive Machine Learning
by: Ren, Yutian, et al.
Published: (2024)