Causal Intervention

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It asks what would happen if a variable or mechanism were deliberately changed, rather than merely observed, to estimate effects, test explanations, or choose actions. Common approaches include structural causal models and the do-calculus, randomized or targeted experiments, hard- and soft-intervention causal discovery, and counterfactual or Bayesian optimization methods that select interventions under costs. Applications range from gene perturbation and clinical policies to dynamical systems, robot control, and audits of neural-network features, evaluated with synthetic ground truth and real experimental or time-series data. Key challenges include limited or unknown intervention targets, hidden confounding and feedback, and whether conclusions transfer across environments; observational fit alone often cannot establish what an intervention will do.

Latest papers

89 papers4 papers in the last four weeks, down 50% 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
Score-Based Learning of Cluster DAGs from Interventions
Oct 8, 2026Gaetano Tedesco, Alex MarkhamScore-Based Causal Discovery Causal Structure Learning
Informatics Institute, University of Amsterdam · Department of Mathematical Sciences, University of Copenhagen
Market Impact Estimation via Causal Foundation Models
Oct 8, 2026Dennis Thumm, Ying ChenQuantitative Finance Algorithmic Trading
National University of Singapore Singapore, Singapore
Who Verifies the Graph? Misspecification Attacks on Causal Action Verification for Language Agents
Sep 30, 2026Fabio RovaiAI Agent Auditing Model Misspecification
The Tesseract Academy London, United Kingdom
CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes
Sep 30, 2026Yuche Gao, Arik Reuter, Siyuan Guo +3Causal Foundation Models Causal Inference
University of Cambridge, Cambridge, United Kingdom · Max Planck Institute for Intelligent Systems, Tübingen, Germany · Prior Labs +3
Certifying Interventional Agreement Among Observationally Equivalent Causal Models
Sep 26, 2026Sourena Khanzadeh, Daniel Platnick, Marjan Alirezaie +1Causal Inference Causal Intervention
Toronto Metropolitan University · Flybits Labs, Creative AI Hub · MIT Media Lab, Massachusetts Institute of Technology
FOCUS: Fixed-Confidence Online Causal Learning Using Sequential Adaptive Interventions
Sep 26, 2026Haijie Xu, Chen ZhangCausal Discovery Structural Causal Models
Department of Industrial Engineering Tsinghua University Beijing, China
An End-to-End Pipeline for Causal ML with Continuous Treatments: An Application to Financial Decision Making
Sep 24, 2026Javier Moral Hernández, Clara Higuera-Cabañes, Álvaro IbraínCausal Inference Causal Effect Estimation
BBVA, AI Factory Madrid, Spain
LeakScale: Estimating the Causal Effect of Benchmark Exposure
Sep 23, 2026Divyansh SinghCausal Effect Estimation Benchmark Design
University of Florida
Causal Bayesian Optimization: Foundations, Methods, and Applications
Sep 21, 2026Chenfeng Huang, Thuy T. Le, Zixuan Ma +1Bayesian Optimization Causal Inference
Department of Statistics & Data Science University of California, Los Angeles · Department of Mathematics and Statistics California State University, Long Beach · Department of Mathematics North Carolina State University
Intervention Granularity Matters: Coherent Treatment Bundles in Counterfactual Simulation with Clinical World Models
Sep 18, 2026Fangzhou Wang, Yixuan Yang, Camilla Balzarotti +1Causal Intervention Action-Conditioned World Models
Duke University
Does Video Memory Use What It Retrieves? A Causal Audit of Memory Specificity
Sep 10, 2026Aditi Tiwari, Akshit Bhalla, Darshan Prasad +1Memory-Augmented Neural Networks Causal Intervention
University of Illinois Urbana-Champaign · Adobe Research
When Decodability Is Not Enough: Logical Validity Representations, Behavioral Dissociation, and Causal Tests in Language Models
Sep 2, 2026Smitha Muthya Sudheendra, Jaideep SrivastavaLogical Reasoning Representation Probing
University of Minnesota, Twin Cities
Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions
Aug 29, 2026Haijie Xu, Chen ZhangCausal Representation Learning Causal Discovery
Department of Industrial Engineering Tsinghua University Beijing 100084, China
I-FLOP: Fast Learning of Order and Parents from Interventional Data
Aug 28, 2026Liuting Chen, Alex MarkhamCausal Discovery Causal Structure Learning
Department of Mathematical Sciences University of Copenhagen Denmark
Actionable CBFI: Prescriptive Interventional Diagnosis and Interaction-Aware Escalation for Sparse Causal Algorithmic Recourse
Aug 28, 2026Sejong OhFeature Attribution Counterfactual Explanations
Dankook University, Republic of Korea
Causal Modelling of Support Interventions for Student Competency Assessment
Aug 25, 2026Francesca Mangili, Alessandro Antonucci, Rafael CabañasEducational Assessment Causal Intervention
IDSIA - SUPSI Scuola Universitaria Professionale della Svizzera italiana (SUPSI) Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA) Lugano, Switzerland · University of Almería Almería, Spain
Generating Intervention Hypotheses using Explainable Explanations on Graphs: G2I, a Two-Stage Greedy Framework
Aug 24, 2026Mulin Tian, Ajitesh SrivastavaGNN Explainability Graph Counterfactual Explanations
University of Southern California Los Angeles, California, USA · Northeastern University Charlotte, North Carolina, USA
Causal Reasoning with Bipartite Graphical Causal Models
Aug 20, 2026Joris M. MooijCausal Reasoning Causal Intervention
Korteweg-De Vries Institute for Mathematics, University of Amsterdam, Amsterdam, the Netherlands
Graph Surgery and the Do-Operator: A Precise Correspondence for Acyclic Structural Causal Models
Aug 18, 2026Satpreet MakhijaStructural Causal Models Causal Intervention
Ashoka University, Sonipat, India
When Can You Trust Offline Evaluation of Equal-Cost Top-k Allocation? A Controlled, Reproducible Benchmark and Practitioner's Guide
Aug 12, 2026Binshuang LiInverse Probability Weighting Causal Effect Estimation
Independent Researcher
DACRI: Decision-Aware Causal Intervention Ranking for Critical Supply Chains
Aug 11, 2026Shiqi Huang, Jiani He, Dingyan Shang +4Learning to Rank Supply Chain Management
Independent Researcher
From probability to causality in probabilistic logic programming
Aug 7, 2026Zora Wurm, Kilian Rückschloß, Felix WeitkämperProbabilistic Logic Causal Structure Learning
Ludwig-Maximilians-Universität München, Oettingenstraße 67, 80538 München · Eberhard-Karls-Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen · German University of Digital Science, Marlene-Dietrich-Allee 14, 14482 Potsdam
Causal Inference with Unstructured Treatments
Aug 1, 2026Kevin Christian Wibisono, Yixin WangCausal Effect Estimation Causal Inference
Department of Statistics University of Michigan, Ann Arbor
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
Teacher Knows It Best: Spontaneous Symmetry Breaking and Tipping Points in Networked Langevin Dynamics AI Sycophancy
Jul 27, 2026Sayantari Ghosh, Saumik Bhattacharya, Partha Pratim ChakrabartiLLM Sycophancy Langevin Dynamics
National Institute of Technology Durgapur, Durgapur, India · Indian Institute of Technology Kharagpur, Kharagpur, India
Amortized Bayesian Causal Discovery of Extended Factor Graphs
Jul 24, 2026Yichen Gu, Yuxuan Song, Weizhou Qian +2Causal Discovery Probabilistic Graphical Models
Department of Electrical and Computer Engineering University of Michigan Ann Arbor, MI 48109, USA · Department of Computational Medicine and Bioinformatics University of Michigan, Ann Arbor Ann Arbor, MI 48109, USA · Department of Statistics University of Michigan, Ann Arbor Ann Arbor, MI 48109, USA +1
Beyond Directed Acyclic Graphs: Causal Zeros and Causal Differential Equations
Jul 24, 2026Sergei V. KalininStructural Causal Models Causal Identifiability
Department of Materials Science and Engineering, University of Tennessee, Knoxville
Interventional Score Geometry for Causal Inference
Jul 24, 2026Mojtaba EslamiInformation Geometry Structural Causal Models
Post-doctoral Researcher, University of Calgary.
How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming
Jul 23, 2026Kilian Rueckschloss, Felix WeitkaemperProbabilistic Logic Logic Programming
Eberhard Karls Universitaet Tuebingen · University of Tübingen Tübingen, Germany · German University of Digital Science +1

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.