Causal Counterfactual Generation

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Causal counterfactual generation asks what would have happened to the same unit or system under a different action, treatment, or feature value. Methods often use structural causal models and abduction-action-prediction, alongside latent-variable models, optimal transport, flow matching, and diffusion to generate alternatives in images, time series, text, and interactive worlds. Work also builds benchmarks with known interventions or paired outcomes to test causal validity, realism, preservation of unchanged features, and performance under distribution shift. A central challenge is balancing plausible, localized edits with faithful causal mechanisms, especially when counterfactuals are not identifiable from observational data alone.

Latest papers

47 papers6 papers in the last four weeks, down 40% on the four weeks before. 0.0% of all new papers.

Intervene, Calibrate, Discover: Learning Causal Structure from Pixels
Oct 26, 2026Ziyi Ding, Xiaodi Huang, Xianxin Lai +4Causal Representation Learning Causal Discovery
Tsinghua University · INFIFORCE Intelligent Technology, China · Institute of Automation, Chinese Academy of Sciences +2
CaMeWM: A Causal Medical World Model for Generative Tumor Evolution and Treatment Planning
Oct 17, 2026Huanlong Gao, Xuelei He, Fengjun Zhao +1Causal World Models Causal Counterfactual Generation
School of Electronic Information, Northwest University, No. 1 Xuefu Avenue, Xi’an, 710127, China · Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, No. 1 Xuefu Avenue, Xi’an, 710127, China
lepidoptera: A Domain-Agnostic Engine for Causal Simulation with Mixed Variable Types, Vine Copula Dependence, and Individual-Level Counterfactual Propagation
Oct 16, 2026Danilo Augusto SartiCausal Counterfactual Generation Structural Causal Models
Lecturer in AI and Machine Learning, Royal College of Surgeons in Ireland, Dublin, Ireland
Causal Spatio-Temporal Neural Distance Fields for Counterfactual Cardiac Anatomy
Oct 15, 2026Mathias Lowes, Kurt Butler, Kristine Aavild Sørensen +4Counterfactual Image Generation Causal Counterfactual Generation
DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark · Causality in Healthcare AI Hub (CHAI), UK · School of Engineering, University of Edinburgh, Edinburgh, UK +2
CoBe: A Benchmark For Conversational Counterfactual Text Editing
Oct 9, 2026Arvind Raghavan, Inwoo Hwang, Shreyas Havaldar +6Causal Counterfactual Generation Counterfactual Reasoning in Language Models
Causal Artificial Intelligence Lab Columbia University
Probabilistic Counterfactual Inference for Discrete Outcomes in Gaussian-Process Causal Models
Oct 6, 2026Juliette Sinnott, Amir-Hossein Karimi, Mohammad KohandelStructural Causal Models Causal Counterfactual Generation
Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada · Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada · Vector Institute for Artificial Intelligence, Toronto, Ontario, M5G 0C6, Canada
Counterfactual Generation via Flow Matching: Coupling-Sensitive End-to-End Rates
Oct 1, 2026Yunrui Guan, Krishnakumar Balasubramanian, Shiva Prasad KasiviswanathanFlow Matching Causal Counterfactual Generation
Department of Applied Mathematics and Statistics, Johns Hopkins University. · Amazon · Department of Statistics, University of California, Davis.
Counterfactual Self-Evolving Agents for Evidence-Grounded Reasoning
Sep 26, 2026Xing Han, Yuxin Wang, Chen Chen +8Self-Evolving Agents Evidence-Grounded Reasoning
Johns Hopkins University · MIT · University of Texas at Austin +1
OneWorld: Learning Consistent Physics Across Actions in World Models
Sep 25, 2026Ke He, Yichen Ding, Bin YangAction-Conditioned World Models Physical Consistency in Video Generation
Wuhan University Wuhan, China
TrafficImag: A Benchmark for Counterfactual Roadside Traffic Video Generation
Sep 25, 2026Xiangyu Li, Tianyi Wang, Zhihao Dou +2Video Generation Evaluation Causal Counterfactual Generation
Fariborz Maseeh Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, Austin, TX 78712, USA · Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA
Conditional Tensor Diffusion: Distributional Counterfactual Learning and Inference
Sep 22, 2026Xinbing Kong, Zeyu Li, Junfan Mao +1Causal Counterfactual Generation Low-Rank Matrix Decomposition
Southeast University · University of Science and Technology of China
Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Models
Sep 21, 2026Xiaodan Xing, Rajat R. Rasal, Julia A. Meister +3Medical Imaging Foundation Models Causal Counterfactual Generation
GSK · Imperial College London
MedDiME: Efficient Latent Diffusion with Adaptive Masking for Medical Counterfactual Generation
Sep 14, 2026Yan Zeng, Changlu Guo, Anders Nymark Christensen +2Counterfactual Explanations Latent Diffusion Models
Department of Applied Mathematics and Computer Science, Technical University of Denmark
To do( $x$ ) or not to do( $x$ ): Medical Image Counterfactuals for Dataset Augmentation
Sep 12, 2026Yasin Ibrahim, Robin J. Evans, Konstantinos KamnitsasFairness in Medical Image Classification Counterfactual Data Augmentation
Department of Engineering Science, University of Oxford · Department of Statistics, University of Oxford
MUtE: A Dual Framework for Concept Erasure and Counterfactual Interventions
Sep 10, 2026Antoine SaillenfestCausal Counterfactual Generation Causal Interventions in Language Models
onepoint, 29 rue des Sablons, 75116 Paris (France)
From Explanations to Interventions: Execution-Guided Counterfactual Synthesis in Temporal Graphs
Sep 10, 2026Minwoo Yu, Young-guk HaCounterfactual Explanations Causal Counterfactual Generation
Smart Computing Laboratory, Department of Computer Science & Engineering, Konkuk University, Seoul 05029, Republic of Korea
AVCG: A Generalized Variational Framework for Counterfactual Generation under Hypothesis Distributions
Sep 7, 2026Jamie Duell, Alejandro Jimenez Rodriguez, Mahault AlbarracinCounterfactual Explanations Causal Counterfactual Generation
School of Computing and Digital Technologies, Sheffield Hallam University · Centre of Excellence in AI and Robotics (CEAIR), Sheffield Hallam University · 3Laboratoire d’analyse cognitive de l’information (LANCI), Université du Québec à Montréal +2
How Can Driving World Models Do Counterfactual Prediction?
Aug 12, 2026Jiaru Zhang, Can Cui, Yi Xu +3Counterfactual Evaluation Causal Counterfactual Generation
Purdue University · Bosch Center for Artificial Intelligence
Twin Rollouts: Noise-Coupled Counterfactual Branching in Interactive Video World Models
Aug 10, 2026Yu Ma, Hongli Shi, Xinran XuAction-Conditioned Video Generation Counterfactual Evaluation
A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning
Aug 9, 2026Jianhan Zhang, Jitao Wang, John D. Piette +3Causal Counterfactual Generation Algorithmic Fairness
University of Michigan · LSE
Causal State-Space Model for Causal Inference: Estimating Longitudinal Individual Treatment Effects
Aug 8, 2026Abisoye Abidakun, Mingjun Zhong, Georgios LeontidisCausal Effect Estimation Selective SSMs
Department of Computing Science, University of Aberdeen, Aberdeen, UK · UiT The Arctic University of Norway
Orca: Neural Operators for Causal Reasoning in Continuous Time
Jul 30, 2026Gerrit Großmann, David A. Selby, Sebastian J. VollmerStructural Causal Models Causal Counterfactual Generation
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany
DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series
Jul 29, 2026Dennis Thumm, Billy Tim Anthony, Ying ChenCounterfactual Evaluation Structural Causal Models
National University of Singapore, Singapore, Singapore
Are Text-to-Image Models Inductivist Turkeys? A Counterfactual Benchmark for Causal Reasoning
Jun 23, 2026Jiayi Lei, Yuandong Pu, Xingyu Han +8Causal Counterfactual Generation T2I Generation
Shanghai Jiao Tong University · Shanghai AI Laboratory · The Chinese University of Hong Kong
Residual-Space Evolutionary Optimization via Flow-based Generative Models
Jun 18, 2026Zhuo Cao, Lena Krieger, Fernanda Nader +3Evolutionary Optimization Causal Counterfactual Generation
IAS-8, Forschungszentrum Jülich, Germany · LMU Munich, Munich Center for Machine Learning (MCML), Germany · Department of Computer Science, Aarhus University, Denmark.
ConTex: Reformulating Counterfactual Generation For Time Series Forecasting
Jun 16, 2026Jan Voets, Hasan Tercan, Tobias Meisen +1Explainable Time Series Forecasting Counterfactual Explanations
Institute for Technologies and Management of Digital Transformation University of Wuppertal
scCBGM: Interpretable Single-Cell Counterfactual Editing
Jun 5, 2026Alma Andersson, Aya Abdelsalam Ismail, Edward De Brouwer +6Concept Bottleneck Models Causal Counterfactual Generation
Genentech · Guide Labs · Department of Computer Science, New York University +1
Are Good Generators Good Decision-Makers? Policy Learning for General Interventions via Retargeted Counterfactual Generation
Jun 5, 2026Raphael C Kim, Jingsen Zhu, Ramin Zabih +1Reinforcement Learning Causal Counterfactual Generation
Cornell Tech, Cornell University, New York, NY · Department of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY
What-If World: A Causal Benchmark for General World Models in Embodied Scenarios
May 26, 2026Kunlin Cai, Rui Song, Jinghuai Zhang +7Physical Consistency in Video Generation Causal World Models
1UCLA · University of Tennessee · 3Amazon
Leveraging Foundation Models for Causal Generative Modeling
May 22, 2026Aneesh Komanduri, Xintao WuCausal Counterfactual Generation Image Generation
University of Arkansas Fayetteville, Arkansas, USA

Oct 26, 2026J/K move · Enter open · S save

Intervene, Calibrate, Discover: Learning Causal Structure from Pixels

Ziyi Ding, Xiaodi Huang, Xianxin Lai, Haijier Chen+3

Tsinghua University · INFIFORCE Intelligent Technology, China · Institute of Automation, Chinese Academy of Sciences+2

Causal Representation Learning Causal Discovery Causal Intervention Causal Counterfactual Generation

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Learning high-level causal variables from low-level observations is a central problem for artificial intelligence. With known node semantics but an unknown graph, visual readout errors can misalign model settings with environment intervention values. We address this mismatch by pairing image readouts with verified values attained through fixed round-robin interventions. With encoder and decoder frozen, a dimension-wise invertible calibration map puts visual responses and target settings in shared numerical units. After freezing this map, the agent selects interventions to resolve graph directions and fits local mechanisms to predict consequences; inverse calibration returns counterfactual states to the decoder's original coordinates. Across Domino, Pick-and-Place, Balance, Gears, CAUSAL3D, and CausalWorld, we evaluate graph discovery, intervention consequences, and counterfactual images. Controlled domino experiments show that interventions resolve orientation ambiguity left by equal additional observations. In a matched-data domino control, calibration reduces the mean error of predicted changes in other variables within the same factual context, relative to an uncalibrated model.