Causal Reasoning

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Causal reasoning asks what produces an outcome, what would change under an intervention, and why an event occurred. Researchers use structural causal models and graphs, discovery and effect-estimation methods, do-calculus, and counterfactual approaches to explanation and responsibility. Recent work also tests and builds language, vision, and agent systems using explicit causal graphs, reasoning traces, simulators, and interactive tasks. A central challenge is distinguishing genuine use of causal structure from reliance on familiar wording or correlations, while making assumptions and uncertainty explicit.

Latest papers

79 papers6 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

Beyond Simpson's Paradox: A Cascade of Confounders in AI Agent Pull-Request Co-Authorship
Jun 21, 2026Haoran Yu, Xiaochong Jiang, Lifei Liu +3Software Engineering AI Coding Agents
Independent Researcher Seattle, WA, USA · Carnegie Mellon University Pittsburgh, PA, USA · Georgia Institute of Technology Atlanta, GA, USA
Faithful Action-unit Causal Reasoning for Counterfactually Faithful Emotion Explanations
Jun 14, 2026Van Thong Huynh, Hong Hai Nguyen, Thuy Pham +2Counterfactual Evaluation Counterfactual Explanations
Faculty of CSE, Ho Chi Minh City University of Technology (HCMUT), VNUHCM, Vietnam · Dept. of AI, FPT University, Vietnam · Faculty of DSAI, College of Technology, National Economic University, Vietnam +1
Graphical Causal Reasoning for Root Cause Analysis in Cloud Networks
Jun 11, 2026Fabien Chraim, Dominik Janzing, John EvansTime-Series Causal Discovery Cloud Computing
Amazon Web Services
WISE: A Long-Horizon Agent in Minecraft with Why-Which Reasoning
Jun 11, 2026Renmin Cheng, Changhao ChenCausal Reasoning Long-Horizon Agent Tasks
The Hong Kong University of Science and Technology · The Hong Kong University of Science and Technology (Guangzhou)
Human Adults and LLMs as Scientists: Who Benefits from Active Exploration?
Jun 4, 2026Mandana Samiei, Eunice Yiu, Anthony GX-Chen +5Cognitive Modeling Causal Reasoning
Mila - Quebec AI Institute · McGill University · University of California Berkeley +4
Caliper: Probing Lexical Anchors versus Causal Structure in LLMs
Jun 3, 2026Zhenyu Yu, Shuigeng ZhouShortcut Learning in Language Models Causal Reasoning in Language Models
Fudan University
Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphs
Jun 2, 2026Clément Yvernes, Emilie Devijver, Marianne Clausel +1Causal Effect Estimation Causal Identifiability
Univ Grenoble Alpes, CNRS, Grenoble INP, LIG, France · Université de Lorraine, CNRS, CRAN, 54000 Nancy, France.
Evaluating Bivariate Causal Statements Based on Mutual Compatibility
May 29, 2026Erik Jahn, Dominik JanzingStructural Causal Models Causal Reasoning
1California Institute of Technology, USA · 2Amazon Research Tübingen, Germany
BEAMS: Benchmarking and Evaluating AI for Modeling and Simulation
May 27, 2026Sara Metcalf, William SchoenbergAI for Science Human-in-the-Loop AI
ISEE Systems
Causal Tongue-Tie: LLMs Can Encode Causal Direction, But Their Yes/No Outputs Fail to Express
May 25, 2026Ziyi Ding, Xiao-Ping ZhangLLM Evaluation Causal Reasoning in Language Models
Tsinghua Shenzhen International Graduate School, Tsinghua University Shenzhen, China
Towards a Universal Causal Reasoner
May 24, 2026Qirun Dai, Xiao Liu, Jiawei Zhang +3Causal Reasoning in Language Models Causal Reasoning
1The University of Chicago · University of Illinois Urbana-Champaign
Causal Past Logic for Runtime Verification of Distributed LLM Agent Workflows
May 20, 2026Benedikt BolligLLM Agent Orchestration Agentic Workflows
Universit´e Paris-Saclay, CNRS, ENS Paris-Saclay, LMF, Gif-sur-Yvette, France
MixRea: Benchmarking Explicit-Implicit Reasoning in Large Language Models
May 19, 2026Yuanqing Cai, Ziyi Huang, Minhao Liu +3LLM Evaluation LLM Prompting
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China
Causely: A Causal Intelligence Layer for Enterprise AI A Benchmark Study on SRE and Reliability Workflows
May 18, 2026Dhairya Dalal, Endre Sara, Ben Yemini +2Software Engineering AI Agent Benchmarks
Causely
Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform
May 13, 2026Feisal Alaswad, Batoul Aljaddouh, Maher Alrahhal +2Latent Dynamics Modeling Long-Horizon Agent Evaluation
Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur 603203, Tamil Nadu, India · Bio-Sensing and Bio-Sensors Group Smart Automation and Communication Technologies Research Institute of Sciences and Engineering University of Sharjah, UAE · Department of Computer Engineering College of Computing and Informatics University of Sharjah, UAE
Counterfactual Reasoning for Causal Responsibility Attribution in Probabilistic Multi-Agent Systems
May 13, 2026Chunyan Mu, Muhammad NajibAlgorithmic Accountability Causal Attribution
University of Aberdeen · Heriot-Watt University
CommonWhy: A Dataset for Evaluating Entity-Based Causal Commonsense Reasoning in Large Language Models
May 13, 2026Armin Toroghi, Faeze Moradi Kalarde, Scott SannerAbductive Reasoning Causal Reasoning in Language Models
University of Toronto · Toronto, ON, Canada
PROMETHEUS: Automating Deep Causal Research Integrating Text, Data and Models
May 13, 2026Sridhar MahadevanCausal World Models Causal Attribution
Adobe Research · University of Massachusetts, Amherst
CauSim: Scaling Causal Reasoning with Increasingly Complex Causal Simulators
May 9, 2026Nicolás Astorga, Anita Kriz, Mihaela van der SchaarStructural Causal Models Knowledge Augmentation for Language Models
DAMTP, University of Cambridge, Cambridge, UK
Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations
May 9, 2026Leopoldo BertossiExplainable Artificial Intelligence Actual Causality
Carleton University, Canada · IMFD, Chile · Emeritus Professor. Adjunct Professor Western University, Canada.
FactoryBench: Evaluating Industrial Machine Understanding
May 8, 2026Yanis Merzouki, Coral Izquierdo, Matei Ignuta-Ciuncanu +8Manufacturing LLM Evaluation
ETH Zurich · Forgis · UC3M +4
Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks
May 8, 2026Gandharv Patil, Keyi Tang, Raquel Aoki +1Causal Effect Estimation Causal Identifiability
McGill University/Mila · RBC Borealis
On Semantic Loss Fine-Tuning Approach for Preventing Model Collapse in Causal Reasoning
May 6, 2026Pratik Deshmukh, Atirek GuptaLLM Fine-Tuning Model Collapse
Technical University of Vienna, Austria · HCLTech, Noida, India
NoisyCausal: A Benchmark for Evaluating Causal Reasoning Under Structured Noise
May 5, 2026Zhi Xu, Yun FuStructural Causal Models Causal Reasoning in Language Models
Northeastern University
SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification
May 5, 2026Zhifeng Hao, Zhongjie Chen, Junhao Lu +5Causal Reasoning in Language Models Causal Reasoning
School of Computer Science, Guangdong University of Technology · College of Mathematics and Computer, Shantou University · Huawei Noah’s Ark Lab +1
KARMA-MV: A Benchmark for Causal Question Answering on Music Videos
May 5, 2026Archishman Ghosh, Abhinaba Roy, Dorien HerremansAudio-Visual Understanding Video QA
AMAAI Lab, Singapore University of Technology and Design
Shadow-Loom: Causal Reasoning over Graphical World Models of Narratives
May 4, 2026David WilmotCausal World Models Interactive Storytelling
Causal Software Engineering: A Vision and Roadmap
May 4, 2026Roberto Pietrantuono, Luca Giamattei, Stefano Russo +2Software Engineering Causal Effect Estimation
University of Naples Federico II · Naples, Italy · Fraunhofer IESE +3
To Use AI as Dice of Possibilities with Timing Computation
May 1, 2026Jia Li, Vipin Kumar, Rui ZhangCausal World Models Time-Series Causal Discovery
Department of Surgery, University of Minnesota, Minneapolis, United States · Department of Computer Science & Engineering, University of Minnesota, Minneapolis, United States
Grounding Before Generalizing: How AI Differs from Humans in Causal Transfer
Apr 27, 2026Liangru Xiang, Yuxi Ma, Zhihao Cao +2LLM Grounding Causal Structure Learning
Department of Automation, Tsinghua University · State Key Laboratory of General Artificial Intelligence · Institute for Artificial Intelligence, Peking University +2

Jun 21, 2026J/K move · Enter open · S save

Beyond Simpson's Paradox: A Cascade of Confounders in AI Agent Pull-Request Co-Authorship

Haoran Yu, Xiaochong Jiang, Lifei Liu, Su Wang+2

Independent Researcher Seattle, WA, USA · Carnegie Mellon University Pittsburgh, PA, USA · Georgia Institute of Technology Atlanta, GA, USA

Software Engineering AI Coding Agents Causal Reasoning Human-AI Collaboration Mining Software Repositories

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Pooled across five AI coding agents, pull requests (PRs) with a human Co-Authored-By trailer merge less often than purely-autonomous ones (53.8% vs. 79.8%) -- yet this aggregate finding is a textbook Simpson's Paradox. Stratifying 33,596 PRs from the AIDev dataset by agent identity reverses the conclusion: Copilot and Devin show large positive within-agent gaps (+41.2 and +33.5 pp, both p<0.001), while Cursor, Claude Code, and Codex show small effects whose cross-sectional 95% CIs span zero. The paradox is driven entirely by agent composition: Codex, which dominates 64.9% of the dataset, achieves high merge rates while rarely using co-authorship. But Simpson's Paradox is only the first layer of a cascade of confounders: within-repo controls eliminate Devin's gap (+33.5 to +1.6 pp, p=0.73); a commit-count control further halves Copilot's within-repo gap (+36.2 to +24.4 pp); restricted to multi-commit PRs, the Copilot within-repo effect dissolves to +4.8 pp (p=0.59). No agent retains a clear co-authorship effect once both repository selection and PR structure are controlled. Our findings caution against reporting agent-pooled statistics without stratification and demonstrate that cross-sectional co-authorship associations are largely selection and PR-structure artefacts rather than evidence of a causal benefit.