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.

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

Ideological Bias in LLMs' Economic Causal Reasoning
Apr 23, 2026Donggyu Lee, Hyeok Yun, Jungwon Kim +4Political Bias in Language Models Language Model Bias Evaluation
Graduate School of Data Science, KAIST, Daejeon, South Korea · College of Business, KAIST, Daejeon, South Korea · School of Computing, KAIST, Daejeon, South Korea +1
ClimateCause: Complex and Implicit Causal Structures in Climate Reports
Apr 16, 2026Liesbeth Allein, Nataly Pineda-Castañeda, Andrea Rocci +1Relation Extraction Climate Science
Department of Computer Science, KU Leuven, Belgium · Institute of Argumentation, Linguistics, and Semiotics (IALS), Universit`a della Svizzera italiana, Switzerland
Generating Effective CoT Traces for Mitigating Causal Hallucination
Apr 14, 2026Yiheng Zhao, Jun YanLLM Hallucination Mitigation Hallucination in Language Models
Concordia University, Montreal, Canada
Hume's Representational Conditions for Causal Judgment: What Bayesian Formalization Abstracted Away
Apr 3, 2026Yiling WuCausal Reasoning
BridgeM, Inc. · Department of Philosophy, University of Massachusetts Amherst
The Digital Twin Counterfactual Framework: A Validation Architecture for Simulated Potential Outcomes
Apr 1, 2026Olav LaudyCounterfactual Evaluation Causal Effect Estimation
LogiStory: A Logic-Aware Framework for Multi-Image Story Visualization
Mar 30, 2026Chutian Meng, Fan Ma, Chi Zhang +3Visual Storytelling Causal Reasoning
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
ViGoR-Bench: How Far Are Visual Generative Models From Zero-Shot Visual Reasoners?
Mar 26, 2026Haonan Han, Jiancheng Huang, Xiaopeng Sun +7Generative AI Evaluation Physical Reasoning
Tsinghua University · Meituan M17 · The University of Hong Kong +1
HCP-DCNet: A Hierarchical Causal Primitive Dynamic Composition Network for Self-Improving Causal Understanding
Mar 12, 2026Ming Lei, Shufan Wu, Christophe BaehrCausal Reasoning Causal Abstraction
Shanghai JiaoTong University, Shanghai · M´et´eo France/CNRS, Universit´e de Toulouse, CNRM UMR 3589, Toulouse, France
De-paradox Tree: Breaking Down Simpson's Paradox via A Kernel-Based Partition Algorithm
Mar 2, 2026Xian Teng, Yu-Ru LinCausal Inference Causal Reasoning
University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Seeking Necessary and Sufficient Information from Multimodal Medical Data
Feb 27, 2026Boyu Chen, Weiye Bao, Junjie Liu +5Medical Image Representation Learning Causal Reasoning
University College London, London, UK · Imperial College London, London, UK · Mingdu Tech, China +1
CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation
Feb 24, 2026Ayush Sawarni, Jiyuan Tan, Vasilis SyrgkanisCausal Effect Estimation Benchmark Design
Stanford University
CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching
Feb 23, 2026Yuzhe Wang, Yaochen Zhu, Jundong LiLLM Evaluation Causal Reasoning in Language Models
University of Virginia Charlottesville, Virginia, USA
DoAtlas-1: A Causal Compilation Paradigm for Clinical AI
Feb 22, 2026Yulong Li, Jianxu Chen, Xiwei Liu +9Causal Reasoning Causal Inference
Mohamed bin Zayed University of Artificial Intelligence · Xi’an Jiaotong-Liverpool University.
ChainReaction: Causal Chain-Guided Reasoning for Modular and Explainable Causal-Why Video Question Answering
Aug 28, 2025Paritosh Parmar, Eric Peh, Basura FernandoVideo QA Multimodal CoT Reasoning
Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore · Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore
Synergizing Large Language Models and Theory for Human-like Causal Reasoning
May 13, 2025Yanxi Zhang, Xin Cong, Zhong Zhang +3Causal Reasoning in Language Models Causal Reasoning
Center for Data Science, AAIS, Peking University · Wangxuan Institute of Computer Technology, Peking University · Tsinghua University +1
A Communication-First Account of Explanation
May 6, 2025Jacqueline Harding, Tobias Gerstenberg, Thomas IcardCausal Reasoning Causal Explanation
Stanford University
Causal Explanations for Image Classifiers
Nov 13, 2024Hana Chockler, David A. Kelly, Daniel Kroening +1Actual Causality Explainable Image Classification
King’s College London, London, UK · Amazon.com, Inc.,, USA · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE +1
CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
Apr 9, 2024Yu Zhou, Xingyu Wu, Jibin Wu +2LLM Evaluation Causal Discovery
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China · College of Computer Science, Chongqing University, Chongqing 400044, China
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
Apr 1, 2024Paritosh Parmar, Eric Peh, Ruirui Chen +4Video QA Causal Reasoning
Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore · Nanyang Technological University · National University of Singapore +1

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Ideological Bias in LLMs' Economic Causal Reasoning

Donggyu Lee, Hyeok Yun, Jungwon Kim, Junsik Min+3

Graduate School of Data Science, KAIST, Daejeon, South Korea · College of Business, KAIST, Daejeon, South Korea · School of Computing, KAIST, Daejeon, South Korea+1

Political Bias in Language Models Language Model Bias Evaluation Causal Reasoning Causal Inference

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Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects? As LLMs are increasingly used in policy analysis and economic reporting, where directionally correct causal judgments are essential, this question has direct practical stakes. We present a systematic evaluation by extending the EconCausal benchmark with ideology-contested cases - instances where intervention-oriented (pro-government) and market-oriented (pro-market) perspectives predict divergent causal signs. From 10,490 causal triplets (treatment-outcome pairs with empirically verified effect directions) derived from top-tier economics and finance journals, we identify 1,056 ideology-contested instances and evaluate 20 state-of-the-art LLMs on their ability to predict empirically supported causal directions. We find that ideology-contested items are consistently harder than non-contested ones, and that across 17 of 20 models, accuracy is systematically higher when the empirically verified causal sign aligns with intervention-oriented expectations than with market-oriented ones. Moreover, when models err, their incorrect predictions disproportionately lean intervention-oriented, and this directional skew is not eliminated by one-shot in-context prompting. These results highlight that LLMs are not only less accurate on ideologically contested economic questions, but systematically less reliable in one ideological direction than the other, underscoring the need for direction-aware evaluation in high-stakes economic and policy settings.