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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Beyond Post-hoc Explanation: Toward Glassbox AI via Probabilistic Mediation
Jun 5, 2026Manuele LeonelliExplainability Artificial Intelligence Governance
Transfer learning for causal forest
Jun 5, 2026Bérénice-Alexia Jocteur, Véronique Maume-Deschamps, Pierre RibereauCausal Inferences Heterogeneous Treatment Effects
When Attribution Patching Lies: Diagnosis and a Second-Order Correction
Jun 5, 2026Luyang Zhang, Jialu WangTraining Data Attribution Mechanistic Interpretability
polyDAG: Polynomial Acyclicity Constraints for Efficient Continuous Causal Discovery in Visual Semantic Graphs
Jun 5, 2026Wenhao Zhang, Ramin Ramezani, Tao Han +2Directed Acyclic Graph Causal Foundation Models
MediEncoder: Nonlinear Representation Learning for High-Dimensional Causal Mediation Analysis
Jun 5, 2026Shi Bo, Debarghya Mukherjee, AmirEmad GhassamiCausal Mediation Analysis Covariate Shift
Human Adults and LLMs as Scientists: Who Benefits from Active Exploration?
Jun 4, 2026Mandana Samiei, Eunice Yiu, Anthony GX-Chen +5Abductive Reasoning Cognitive Science
Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs
Jun 4, 2026Hazhir Aliahmadi, Irina Babayan, Greg van AndersBayesian Networks Directed Acyclic Graph
Discrete Causal Representations from Heterogeneous Domains: A Bayesian Approach with Social Survey Applications
Jun 4, 2026Ankur Garg, Michael Stettler, Aaron Schein +1Causal Representation Learning Subspace Intervention Framework
Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming
Jun 4, 2026Shah Pallav Dhanendrakumar, Saikat Pal, Sitikantha RoyNeural Ordinary Differential Equations Differentiable Programming
LLM Explainability with Counterfactual Chains and Causal Graphs
Jun 4, 2026Nirit Nussbaum-Hoffer, Nitay Calderon, Liat Ein-Dor +1Causal Graph Explainability
Causal Scaffolding for Physical Reasoning: A Benchmark for Causally-Informed Physical World Understanding in VLMs
Jun 4, 2026Tianyi Tang, Zhuoyi Lin, Zeyu Feng +4Large Vision Language Models Causal Reasoning
EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning
Jun 4, 2026Sota AsanumaCausal Foundation Models Mechanistic Interpretability
TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning
Jun 4, 2026Etienne Le Naour, Tahar Nabil, Adrien PetraliaTime Series Foundation Models Time Series
Causal Longitudinal Prior-Fitted Networks for Counterfactual Outcome Prediction
Jun 4, 2026Amirhossein Zare, Amirhessam Zare, Herlock Rahimi +2Causal Inferences Counterfactual Reasoning
Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions
Jun 4, 2026Wenhao Mu, Facundo Yan, Anik Mumssen +2Epidemic Forecasting Causal Inferences
Causal Modeling of Selection in Evolution
Jun 4, 2026Haoyue Dai, Zeyu Tang, Peter Spirtes +1Selection Bias Evolution
StableRCA: Robust Graph-Agnostic Mechanism-Level Root Cause Analysis
Jun 4, 2026Xiaoyu Lin, Nicholas Tagliapietra, Kehan Li +2Root Cause Analysis Causal Graph
From Prediction to Self: Developmental Conditions for Agency in Minimal Neural Systems
Jun 4, 2026Evan YeNext Activity Prediction Self-Organization
CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting
Jun 3, 2026Zhaoqi Zhang, Miao Xie, Yi Li +3Causal Graph Causal Reasoning
LMT: A Bayesian Framework for Causal Discovery from Textual Alarm Records in Manufacturing Systems
Jun 3, 2026Xiaofeng Xiao, Jianhong Chen, Qiuzhuang Sun +2Causal Foundation Models Early Warning
Environment-Robust Representation Learning with Empirical Bayes
Jun 3, 2026Yuli Slavutsky, Matthew Shen, Bohan Wu +1Bayesian Latent Variable
Validity Threats for Foundation Model Research
Jun 3, 2026Gunnar König, Martin Pawelczyk, Ulrike von Luxburg +1Foundation Model Validity
Clinical Assistant for Remote Engagement Link (CARE-link): A Web-Based Electronic Health Records Software for Managing Diabetes
Jun 3, 2026Prince Ebenezer Adjei, Joshua Teye Tettey, Toufiq Musah +2Electronic Health Records Clinical
Caliper: Probing Lexical Anchors versus Causal Structure in LLMs
Jun 3, 2026Zhenyu Yu, Shuigeng ZhouCausal Reasoning Instruction-Tuned Models
Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Models
Jun 3, 2026Dario Rancati, Max Welling, Francesco LocatelloStructural Causal Model Statistical Mechanics
Trivium: Temporal Regret as a First-Class Objective for Causal-Memory Controllers
Jun 3, 2026Edward Y. ChangStructural Causal Model Causal Reasoning
Motion-Guided Causal Disentanglement for Robust Multi-View Cine Cardiac MRI Diagnosis
Jun 3, 2026Chuankai Xu, Cristiane De Carvalho Singulane, Mohammad Abuannadi +10Cardiac Magnetic Resonance Imaging Whole-Heart Segmentation
Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings
Jun 2, 2026Linsen Li, Aron Culotta, Nicholas MatteiSentiment Analysis Causal Inferences
Privacy-Robust Incrementality Measurement for Advertising Systems under Signal Loss
Jun 2, 2026Prashant Shekhar, Caroline HowardAds AI Privacy Risks and Protection
Expert-Aware Causal Tracing of Factual Recall in Sparse MoE Language Models
Jun 2, 2026Yuetian Lu, Ali Modarressi, Yihong Liu +1Mixture-Of-Expert Language Models Mixture-of-Experts Models
Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphs
Jun 2, 2026Clément Yvernes, Emilie Devijver, Marianne Clausel +1Causal Graph Causal Reasoning
CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery
Jun 2, 2026Bo Peng, Kaiwen Wu, Sirui Chen +3Causal Foundation Models Large Language Model Reliability
Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
Jun 2, 2026Roman Plaud, Alexandre Perez-Lebel, Antoine Saillenfest +4Causal Inferences Propensity
Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
Jun 2, 2026Gautam Gare, John Galeotti, Michael Mozer +2Causal Graph Covid-19
Learning Temporal Causal Structure via Smooth Differentiable Optimization
Jun 2, 2026Tong Zhao, Ce Guo, Wayne Luk +2Causal Foundation Models Temporal Dependence
DMT-CBT: Longitudinal Therapeutic State Modeling for CBT Counseling
Jun 2, 2026Chang Liu, Shuyi Zhang, Changsheng Ma +3Cognitive Behavioral Therapy Treatment
Conditional Hypothesis Generation for LLM-Based Text Analysis with Researcher-Specified Covariates
Jun 2, 2026Paiheng Xu, Jing Liu, Wei AiHypothesis Generation Computational Social Science
Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families
Jun 1, 2026Roel Hulsman, Carles Balsells-Rodas, Sara MagliacaneCausal Foundation Models Identifiability
CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations
Jun 1, 2026Chengfeng Wu, Tao Zou, Yanru Wu +1Multi--Task Learning Causal Representation Learning
It does what it says on the tin: safe synthetic data from coarsened margins
Jun 1, 2026Gillian M RaabSynthetic Data Covariate Shift
Token Predictors Are Not Planners: Building Physically Grounded Causal Reasoners
Jun 1, 2026Zheng Lu, Mingqi Gao, Qinlei Xie +8Classical Planning Embodied Agents
Consistency evaluation of benchmarks used for causal discovery
Jun 1, 2026Yuzhe Zhang, Chihui Chen, Lina Yao +1Causal Foundation Models Causal Graph
Shortcut to Nowhere: Demystifying Deep Spurious Regression
Jun 1, 2026Guanrong Xu, Jessica Li, Hao Wang +1Spurious Correlations Latent Confounders
Scalable Counterfactual Risk Estimation for Rare Events in Longitudinal Data
Jun 1, 2026Xiaohui Yin, Avijit Mitra, Ying Zhou +2Survival Analysis Causal Inferences
Transferring Information Across Interventions in Causal Bayesian Optimization
May 31, 2026Mohammad Ali JavidianBayesian Optimization Causal Representation Learning
Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention
May 31, 2026Shuochen Chang, Tong Bai, Xiaofeng Zhang +5Efficient Latent Reasoning Black Box
Topological Ignorability for Structural Causal Effects Beyond Means
May 31, 2026Usef FaghihiCausal Inferences Heterogeneous Treatment Effects
Interaction-Limited Safe Continuous-Time RL for Dynamical Medical Treatment
May 31, 2026Xun Shen, Yuepeng Wang, Akifumi Wachi +13Safety Constraints Treatment
A Unified Benchmark for Dynamic Medical Treatment Reinforcement Learning
May 31, 2026Yuepeng Wang, Ken Kawano, Yoshihiko Fujisawa +12Joint Reinforcement Learning Treatment
A Machine-to-Machine Knowledge-Guided LLM Agent for Generalizable Radiotherapy Treatment Planning
May 30, 2026Md Mainul Abrar, Xun Jia, Yujie ChiRadiotherapy Large Language Model Planning
An Exploratory Study into using Machine-Learning for Fast Step-by-step Emulation of Numerical Mechanical Thrombectomy Simulations for Ischemic Stroke
May 30, 2026Thijs StessenEmulators Surrogate Models
Extending Causal Metamodeling to a non-Markovian Queue
May 30, 2026Pracheta Amaranath, Anant Bhide, David Jensen +1Stochastic Delays Bayesian Networks
Causal Density Functions
May 30, 2026Sridhar MahadevanCausal Inferences Density Ratio Estimation
The Paradox of Outcome Optimization: A Causal Information-Theoretic Bound on Reasoning Shortcuts in LLMs
May 30, 2026Zihan Chen, Yiming Zhang, Wenxiang Geng +2Causal Reasoning Information-Theoretic Limits
PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosis
May 30, 2026Zongzong Wu, Ming Zhao, Fengxiao Tang +1Large Language Model Reasoning Causal Reasoning
On the Recoverability of Causal Relations from Bulk Gene Expression Data
May 30, 2026Gongxu Luo, Boyang Sun, Kun ZhangGene-Regulatory Networks Spatial Transcriptomics
Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression
May 30, 2026Kwangho Kim, Jisu KimSemi-Supervised Learning Covariate Shift
Evaluating Bivariate Causal Statements Based on Mutual Compatibility
May 29, 2026Erik Jahn, Dominik JanzingStructural Causal Model
When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework
May 29, 2026Dylan Steiner, Gustavo Arango-Argoty, Gerald Sun +1Multimodal Clinical Data Multimodal Classification
Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
May 29, 2026Naoki Chihara, Tatsushi Oka, Yasuko Matsubara +2Heterogeneous Treatment Effects Covariate Shift

Jun 5, 2026 · cs.AIJ/K move · Enter open · S save

Beyond Post-hoc Explanation: Toward Glassbox AI via Probabilistic Mediation

Manuele Leonelli

School of Science and Technology, IE University, Madrid, Spain

Large language models are rapidly becoming infrastructural components in high-stakes institutional settings, including public administration, legal reasoning, and healthcare, where opacity is not merely inconvenient but institutionally and legally untenable. Existing approaches to explainability are predominantly post-hoc, offering unstable, non-contestable accounts that have no formal relationship to the reasoning process that produced the output. We argue that the problem is not the absence of explanation but the absence of structured reasoning in the first place. This paper makes the case for a fundamentally different architecture, which we call the Glassbox Framework, in which Bayesian networks serve as transparent, ante-hoc mediation layers for generative models. Bayesian networks encode domain knowledge, causal assumptions, and probabilistic dependencies before inference occurs, enabling auditable reasoning traces, uncertainty quantification, and contestable outputs. We characterise the architecture of this framework and ground it in a benefit eligibility scenario, identifying the foundational challenges spanning semantic alignment, dynamic model construction, probabilistic grounding, and human governance that must be solved to realise it at scale. By shifting from post-hoc explanation to ante-hoc probabilistic mediation, this work outlines a principled path toward AI systems that are not only powerful but fundamentally accountable.

Explainability Artificial Intelligence Governance Causal Mediation Analysis Post-Hoc Artificial Intelligence Systems Bayesian Networks

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