Bayesian Modeling and Causal Inference

Neueste Paper

• Structure-agnostic Causal Representation Learning

  Arman Behnam, Binghui Wang · 2. Oktober 2026

  Causal representation learning aims to discover robust features by exploiting the causal structure underlying data generation. Existing methods require specifying the causal structure a priori, yet different structures demand fundamentally incompatible invariance constraints, and misspecification le…

• When Do Causal World Models Help Modular LLM Agents

  Xinyuan Song, Zekun Cai · 2. Oktober 2026

  LLM agents increasingly act through modular systems, such as order, payment, inventory, and shipment services, where actions in one module change which transitions are valid in another. Standard world models usually fit observational traces, but this is not the quantity needed for intervention-time …

• CHAIN: Calibrated LLM Forecasting via Causal-Temporal Hypergraph Inference

  Wenjin Liu, Chenxi Wang, Yue Lu, Zhe Cui, Haoran Luo · 1. Oktober 2026

  Large language models have achieved significant progress in event forecasting, yet their probability outputs exhibit systematic calibration bias that varies heterogeneously across different domains and question types, undermining the trustworthiness of probabilistic outputs for decision-making under…

• Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders

  Sadegh Khorasani, Ali Najar, Saber Salehkaleybar, Negar Kiyavash · 1. Oktober 2026

  We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unknown number of exogenous latent confounders, bounded by a given maximum. We derive the covariance of the observed variables and introduce marginal quasi…

• Who Verifies the Graph? Misspecification Attacks on Causal Action Verification for Language Agents

  Fabio Rovai · 1. Oktober 2026

  Causal action verifiers gate an agent's state-changing tool calls by checking whether each proposed intervention is identifiable against a committed action-state graph, and they issue a certificate that carries the identification argument and a one-sided lower confidence bound. One such verifier, CI…

• Inferring Causal Relations between Two Sequences of Events with Language Models

  Nishchal Prasad, Eric Gaussier, Emilie Devijver, Alexander Obeid Guzman, Armen Aghasaryan, Gregor G\"ossler · 1. Oktober 2026

  Causal AI is a branch of Artificial Intelligence which helps understand and reason about cause and effect relationships, not just patterns or correlations. Causal discovery aims to infer elements of the underlying causal structure--often represented as a directed graph--from observational and, when …

• Do-JEPA: From Masking to Intervention in Latent World Models

  Hossein Resani, Javen Qinfeng Shi · 30. September 2026

  Latent world models are trained to predict what happens next, so nothing in their objective separates what an action caused from what merely co-occurred with it. Object-masking models such as C-JEPA intervene on what the predictor can see; we intervene on what physically happens. From one saved simu…

• Beyond Conditional Independence: Root Cause Analysis with Deep Causal Models

  Md Musfiqur Rahman, Kenneth Lee, Ziwei Jiang, Padmaja Jonnalagedda, Ruocheng Guo, Murat Kocaoglu · 30. September 2026

  Root cause analysis (RCA) is a critical problem in many real-world scenarios. RCA enables the identification of faulty or failing mechanisms in a system by comparing anomalous observations with corresponding reference (i.e., regular) observations. However, existing approaches rely either on heuristi…

• Demistifying Data and Simulator Assumptions in Supervised Causal Discovery

  Pingchuan Ma, Rui Ding, Bojun Huang, Shuai Wang · 30. September 2026

  Supervised causal discovery learns to infer causal structure for a new dataset from training datasets paired with structural labels. These training pairs are typically simulated, making the simulator both a source of supervision and a carrier of assumptions about causal graphs, mechanisms, and noise…

• Active Causal Discovery Benchmark: Evaluating LLM Agents Under Budgeted Interventions

  Sagar Deb, Devam Shah, Ashwanth Krishnan · 29. September 2026

  We introduce the Active Causal Discovery Benchmark (ACDB), an SCM-grounded environment for evaluating whether LLM agents recover causal graph structure from observations and budget-constrained hard interventions. ACDB pairs a linear-Gaussian world generator with a fixed observe-intervene-submit API …

• Certifying Interventional Agreement Among Observationally Equivalent Causal Models

  Sourena Khanzadeh, Daniel Platnick, Marjan Alirezaie, Hossein Rahnama · 29. September 2026

  Observationally equivalent causal models can still disagree about what happens under intervention, because interventions create inputs that never occur in observational data. We introduce Interventional Separation Selection (ISS), which repeatedly queries the true system with an admissible intervent…

• MARCEDES: Score-based causal discovery under non-Gaussianity with continuous optimization

  Anamitra Chaudhuri, Anirban Bhattacharya, Yang Ni · 28. September 2026

  We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) with non-Gaussian errors. Motivated by an intentionally misspecified non-Gaussian SEM with all Laplace errors, we first introduce the mean absolute resi…

• An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making

  Javier Moral Hern\'andez, Clara Higuera-Caba\~nes, \'Alvaro Ibra\'in · 28. September 2026

  This paper presents an end-to-end causal machine learning (ML) pipeline designed for real-world applications with continuous treatments. The proposed framework consists of six sequential steps: dimensionality reduction, causal identification, positivity assumption violation handling, estimation, ref…

• LUCID: Learning Under Confounding for Inference and Discovery in Time Series

  Mohammad Fesanghary · 28. September 2026

  Unobserved common causes are pervasive in real-world time series and can induce spurious associations that causal discovery methods mistake for direct edges. We propose LUCID (Learning Under Confounding for Inference and Discovery, a regime-adaptive deconfounding layer that first estimates the confo…

• Causal Retention in Interactive Agents: Interface Factorization and Selective Adaptation

  Shengjun Zhang, Tingyi Liu, Dong Xie, Yunlong Dong, Xiang Wang, Cheng Zeng · 28. September 2026

  Task performance need not determine which intervention mechanism an agent retains. We study causal retention: whether a frozen learned state answers a mechanism-probe map fixed independently of training, including action, context, direct target, value, and delay. For finite structural causal model c…

• Externalized CPDAG Summaries Improve LLM Causal Deduction

  Wentao Sun, Jo\~ao Paulo Nogueira, Dominique Verchere, Mathieu Acher, Alonso Silva · 28. September 2026

  Corr2Cause asks whether a causal claim holds in every DAG compatible with observed correlations and conditional independencies. We frame this as latent-object reasoning: the label is defined by a CPDAG query, but free-form chain-of-thought often collapses the Markov-equivalence-class problem into lo…

• Auditing Bayesian Graph Alignment: Diagnostic Comparisons and Reference Failure

  Melika Gorgi, Kourosh Mirsohi · 25. September 2026

  Bayesian graph alignment estimates correspondence probabilities, but convergence of an alignment-score trace need not imply accurate correspondence marginals. We audit this gap on 240 new exact graph pairs from four source families, 240 larger pairs with 20-100 vertices, and a separate 60-case exact…

• xWhyL: Causal Interactive Learning

  Nicholas Tagliapietra, Florian Peter Busch, Moritz Willig, Matej Ze\v{c}evi\'c, Lavdim Halilaj, Juergen Luettin, Kristian Kersting · 23. September 2026

  Explanations are central to causal reasoning, and cognitive science has long established that the human drive to explain is itself a mechanism for learning about causality. Despite this, learning from those abductive signals is largely ignored in artificial intelligence. While explainable AI (XAI) i…

• Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Models

  Zijian Li, Ruichu Cai, Feng Xie, Xinshuai Dong, Haoyue Dai, Yuewen Sun, Yujia Zheng, Guangyi Chen, Yingyao Hu, Kun Zhang · 22. September 2026

  Uncovering latent variables and their causal relations from observed data is a fundamental yet challenging problem. Existing methods often rely on restrictive assumptions, such as linear relations or invertible mixing functions. To better address this problem under general nonlinear mixing procedure…

• Decoupled Causal Discovery

  Zhengkang Guan, Fei Wu, Kun Kuang · 22. September 2026

  Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on conditional independence tests, structure scores, or restrictive functional assumptions, we propose Decoupled Causal Discovery (DCD), a novel decoup…

• ITSY: Causal Discovery From Irregular Time-Series Data

  Wenbo Xu, Yue He, Yunhai Wang, Yueguo Chen, Kun Kuang · 22. September 2026

  Structural causal models for time series recover contemporaneous and lagged effects, but most methods require complete observation windows and become misspecified when samples are missing. We introduce ITSY, the first continuous-optimization method for causal discovery from irregular time series und…

• Common Cause, Not Cross-Attention: Blocking Visual Shortcuts in Audio-Video Generation

  Jian Xu, Delu Zeng, John Paisley, Qibin Zhao · 22. September 2026

  Joint audio--video generators are trained on data in which what an event looks like and what it sounds like are strongly, often spuriously, correlated: a particular material, texture, or object appearance co-occurs with a particular sound. This paper is a controlled causal study of the resulting fai…

• CaLR: Causal Latent Revision for Robust Diffusion Reasoning

  Wei Cai, Jian Zhao, Yuchen Yuan, Xuelong Li · 21. September 2026

  Autoregressive (AR) models suffer from local greediness, while diffusion language models (DLMs) often lack the strict causal structure required for reasoning. To combine the advantages and overcome the drawbacks of the dual, we propose Causal Latent Revision (CaLR), a framework that reformulates rea…

• Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms

  Sambit Mishra, Yingying Wang, Christine K. Johnson, Urbashi Mitra · 18. September 2026

  Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptions. Existing identifiability research primarily focuses on continuous variables under additive noise models, often negle…

• MaSCoD: A Multi-Agent Framework for Structural-Context-Guided Candidate Causal Graph Generation

  Yudai Nakada, Yuichiro Nishiura, Jin Michael Splichal · 18. September 2026

  Large language models (LLMs) have been applied to causal discovery, but candidate-graph generation rarely treats premature omission of potentially relevant causal relations as an explicit design objective. We propose MaSCoD, a multi-agent framework that organizes candidate third variables and local …