Causal

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Causal Reasoning

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Covariate Shift

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Causal Foundation Models

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Causal Inferences

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Causal Graph

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Structural Causal Model

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Heterogeneous Treatment Effects

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Latent Confounders

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Treatment

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Propensity

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Causal Representation Learning

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Causal Intervention

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944 papers

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A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning
Jun 15, 2026Ardianto Wibowo, Paulo E Santos, Amer Baghdadi +3Distribution Shifts Transitions
GIST-CMTF: Goal-State Inference for Causal Minimal Tool Filtering in LLM Agents
Jun 15, 2026Rahul Suresh Babu, Rohit ShuklaMulti-Turn Tool-Use Agent Large Language Model Agents
Multi-Modal Spatio-Temporal Graph Neural Network with Mixture of Experts for Soil Organic Carbon Prediction
Jun 15, 2026Daniele Mos, Felipe Drummond, Anton Bossenbroek +1Soil Moisture Estimation Soil
AURA: Active-Response Attribution under Treatment Ambiguity in Bacterial Cytological Profiling
Jun 15, 2026Kartik Jhawar, Mrunmayee Deshpande, Wilfried Moreira +2Bacterial Colony Counting Area Under The Receiver Operating Characteristic Curve
The Critical Role of Model Selection in Causal Inference: A Comparative Analysis of Classification Models within the InferBERT Framework for Pharmacovigilance
Jun 15, 2026Csaba Kiss, Roland Molontay, Gabriele PergolaCausal Inferences Model Selection
Circuit Tracing in Autoregressive Protein Language Models
Jun 14, 2026Darin Tsui, William Deinzer, Daniel Saeedi +1Mechanistic Interpretability Autoregressive Language Models
Causal-Privacy Audit Workflow for Synthetic and Distilled Data in Dropout Support
Jun 14, 2026Hanghang Zheng, Xiwei Zhuang, Zhong Wang +4Causal Inferences Distributional Fidelity
DeepRoot: A KG-Coordinated Multi-Agent System for Therapeutic Reasoning over Historical Medical Texts
Jun 14, 2026Zijian Carl Ma, Sean J. Wang, Sijbren Kramer +1Biomedical Knowledge Graph Clinical Reasoning
Bayesian Networks with Latent Time Embedding for Stage-Aware Causal Modeling of Alzheimer's Disease Progression
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Faithful Action-unit Causal Reasoning for Counterfactually Faithful Emotion Explanations
Jun 14, 2026Van Thong Huynh, Hong Hai Nguyen, Thuy Pham +2Counterfactual Reasoning Causal Reasoning
From Correlation to Causation in Lane Change Prediction for Automated Driving: A Causal Explanation Framework
Jun 14, 2026Mohamed Manzour, Aditya Kumar, Augusto Luis Ballardini +1Causal Reasoning Driving
Vernier: Probing Representational Misalignment Behind Lexical Gaps in Causal Reasoning
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Can Causal Models Enhance Robot Navigation? Online Causal Adaptation for Real-Robot Navigation
Jun 14, 2026Zhitao Liang, Alex Mitrevski, Emmanuel Dean +1Social Robot Navigation Structural Causal Model
SDVDiag: Multimodal Causal Discovery for Online Diagnosis in Software-defined Vehicles
Jun 14, 2026Matthias Weiß, Athreya Hosahalli Prakash, Falk Dettinger +2Root Cause Analysis Fault Injection Experiments
CausalDrive: Real-time Causal World Models for Autonomous Driving
Jun 13, 2026Tianyi Yan, Huan Zheng, Dubing Chen +10Autonomous Driving Simulation Autonomous Driving
Feature Attribution in Directed Acyclic Graphs Using Edge Intervention
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AI for Social Good: An Investigation of the Causal Relationship Between Environmental Regulations and Their Effects on Air Pollution in London, UK
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Relational Structural Causal Models
Jun 12, 2026Adiba Ejaz, Elias BareinboimStructural Causal Model Causal Graph
Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias
Jun 12, 2026Annie Ulichney, Amanda CostonCovariate Shift Selection Bias
CADET: Physics-Grounded Causal Auditing and Training-Free Deconfounding of End-to-End Driving Planners
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Causal Object-Centric Models for Planning with Monte Carlo Tree Search
Jun 12, 2026Rodion Vakhitov, Leonid Ugadiarov, Alexey Skrynnik +1Monte Carlo Tree Search Object-Centric Learning
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Gradient boosting for extremes: sampling theory and application to insurance
Jun 12, 2026Stéphane Lhaut, Olivier LopezGradient Boosted Tree Gaussian Process
Attention-Based Estimation of the Individual Treatment Benefit Probability under Dose Variation
Jun 11, 2026Lev V. Utkin, Andrei V. Konstantinov, Stanislav K. Kogan +2Treatment Effect Heterogeneous Treatment Effects
Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning
Jun 11, 2026Zach Studdiford, Gary LupyanLarge Language Model Reasoning Reasoning Errors
NetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networks
Jun 11, 2026Fabien Chraim, Jian Zhang, Dominik Janzing +3Root Cause Analysis Causal Graph
Graphical Causal Reasoning for Root Cause Analysis in Cloud Networks
Jun 11, 2026Fabien Chraim, Dominik Janzing, John EvansRoot Cause Analysis Causal Graph
Mining Architectural Quality Under Agentic AI Adoption: A Causal Study of Java Repositories
Jun 11, 2026Oliver Aleksander Larsen, Mahyar T. MoghaddamRepository Coding Agents
NTS-CoT: Mitigating Hallucinations in LLM-based News Timeline Summarization with Chain-of-Thought Reasoning
Jun 11, 2026Feng Lyu, Huiqin Yan, Sijing Duan +5Summarization Large Language Model Hallucination
CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts
Jun 11, 2026Bo Liu, Di Dai, Jingwei Liu +5Causal Foundation Models Causal Graph
Circuit Synchronization Precedes Generalization: A Causal Precursor to Grokking
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Where Computation Lives Inside TabPFN: Causal Localisation of Attention Head Function
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Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression
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Observable Patterns Are Not Explanations: A Causal-Geometric Analysis of Latent Reasoning Models
Jun 10, 2026Darpan Aswal, Thomas Palmeira Ferraz, Yongxin Zhou +1Efficient Latent Reasoning Latent Thoughts
Forecasting Is Not Attribution: Localizing Decoder Bypass in Graph-Based Neural Marketing Mix Models
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How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?
Jun 10, 2026Julia Kostin, Kasra Jalaldoust, Elias Bareinboim +2Causal Inferences Domain Adaptation
Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation
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The Impossibility of Eliciting Latent Knowledge
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Generalized Conformal Predictive Systems Under Distributional Shifts
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WorldKernel: A World Model is the Coupling Kernel of Admissible Possible Worlds
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From Observation to Intervention: A Causal Audit of Expert Importance in Mixture-of-Experts Models
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Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting
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Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models
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Capacity, Not Format: Rethinking Structured Reasoning Failures
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Does Normalization Choice Matter for Causal Large Time-Series Models?
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Generative Frontier Planning for Adaptive Peer-Referral Recruitment under Covariate-Dependent Arrivals
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Jun 6, 2026Se Yoon Lee, Yonghyun Kwon, Jae Kwang KimTime-To-Event Data Heterogeneous Treatment Effects
Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures
Jun 6, 2026Jaineet ShahLarge Language Model Agents Counterfactual Reasoning
Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variables
Jun 6, 2026Mariyam Khan, Shohei Shimizu, Thong PhamCausal Foundation Models Identifiability
CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learning
Jun 6, 2026Yongqi Jiang, Yansong Gao, Siguang Chen +1Privacy-Preserving Framework Causal Representation Learning
Identifiability and Estimation for Unlabeled Finite Mixtures under Marginal Independence
Jun 6, 2026Takafumi Kanamori, Yushi Hirose, Shohei YamamotoMixture Models Identifiability
Off-Policy Evaluation with Strategic Agents via Local Disclosure
Jun 5, 2026Kiet Q. H. Vo, Abbavaram Gowtham Reddy, Julian Rodemann +2Off-Policy Evaluation Strategic Agents
A Causal Probabilistic Framework for Perception-Informed Closed-Loop Simulation of Autonomous Driving
Jun 5, 2026Zhennan Fei, Rickard Johansson, Mikael Andersson +12Autonomous Driving Perception Autonomous Driving

Jun 15, 2026 · cs.LGJ/K move · Enter open · S save

A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning

Ardianto Wibowo, Paulo E Santos, Amer Baghdadi, Matthew Stephenson+2

Reinforcement learning (RL) systems often degrade when operating conditions differ from those previously encountered, reflecting distributional shifts in the underlying data-generating process. Such shifts may occur between training and evaluation, as in In-Distribution (ID) and Out-of-Distribution (OOD) generalization, or within non-stationary settings where environment dynamics evolve over time. However, the formal relationship between these views remains unclear, and existing work mainly focuses on mitigation rather than the causal origin of shift within the agent-environment interaction. This work develops a unified causal-origin taxonomy that characterizes sources of distributional shift in RL and relates ID/OOD generalization to non-stationary settings. We transfer the classical dataset-shift principle from supervised learning to RL by reformulating distributional shift in terms of the generative interaction process. Using a Partially Observable Markov Decision Process (POMDP), we decompose the interaction into structural components, including the state distribution, observation process, policy, reward, and transition dynamics, together with the shifted-time boundary. The proposed taxonomy distinguishes internal (agent-driven) and external (environment-driven) distributional shifts. The shifted-time boundary perspective further characterizes explicit, implicit, and hybrid shifts. This formulation unifies ID/OOD generalization and non-stationarity as structured changes in the underlying process. We also introduce an evaluation framework for measuring shift impact and adaptation through performance degradation and recovery metrics. By grounding distributional shift in the causal-origin structure of RL, this work supports systematic analysis of robustness under distributional shift.

Distribution Shifts Transitions Causal Reasoning

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