Bayesian Modeling and Causal Inference

Latest papers

• Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity

  Tatsuya Yamada, Hiroshi Morioka, Yoshinobu Kawahara · 5 October 2026

  Causal representation learning for time-series data aims to identify latent states and their causal relations from observations. In this setting, an important challenge is to model both lagged causal relations across observation intervals and faster causal effects that appear as instantaneous relati…

• To Explore The Strange New World Beyond Data Distribution: System Behavior, Causality Tax, and Non-causal Base Model

  Xianzhi Zeng, Jiangneng Li, Gao Cong · 5 October 2026

  We show that the causality of language models (LMs) may not be necessary nor optimal. This is the case when system behavior (denoted as $S$) is incorporated as a first-principle Bayesian feature. Here, $S$ refers to extra dominant factors beyond the data space, and they involve coupled effects. Desp…

• Shrome at Touch\'e: Soft-Vote Ensembling and Counter-Causal Augmentation for Causality Extraction

  Roham Zendehdel Nobari, Shayan Sooratgar · 5 October 2026

  Touch\'e 2026 extends causality extraction to counter-causal claims: news sentences whose surface form appears causal but whose meaning denies the causation, as in "It is falsely believed that X caused Y." A system that relies on surface cues such as "caused" or "led to" will accept such a sentence …

• Mapping and Advancing the Scalability-Accuracy Frontier of Nonlinear Causal Discovery

  Hendrik Suhr, Sascha Xu, Jilles Vreeken · 5 October 2026

  Scalable nonlinear causal discovery requires methods that combine flexible mechanism estimators with efficient search over large graph spaces. Several algorithmic families have been proposed to address this challenge, yet their accuracy-runtime trade-offs remain poorly understood. We empirically com…

• How Causality Bridges the Semantic Gap

  Shuhao Zhang, Xuran Zhou, Han Guo, Pengtao Xie, Yujia Zheng · 5 October 2026

  Numerical measurements capture how a system behaves, but often leave the meanings of its variables unspecified. Some variables are measured but never labeled, and others are never measured at all. Existing methods assign semantics to such variables by consulting general human knowledge, but this inh…

• Overcoming Challenges of Interpretive Structural Modeling with Large Language Models

  Everett Rush, David J. Icove, Ari Kim, Byung H. Park, Michael A. Langston · 5 October 2026

  Interpretive Structural Modeling (ISM) is a well-known process for multi-criteria decision making. The success of ISM over other methodologies is its ability to model causal relationships, the binary scale of factors, and resulting hierarchical representation. Traditionally, the modeling process is …

• Reasoning Models Are Accurate but Unsound on Identification

  Arman Behnam, Binghui Wang · 5 October 2026

  A reasoning model asked whether a causal effect is recoverable from observational data can fail in two ways: it refuses an identifiable query or answers a nonidentifiable one. The latter is more consequential, as no observational data can validate the claimed formula. Measuring this failure requires…

• Structure-agnostic Causal Representation Learning

  Arman Behnam, Binghui Wang · 2 October 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 October 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 October 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 October 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 October 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 October 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…