Causal Identifiability

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Causal identifiability studies whether observations, interventions, or other information determine a causal graph or quantity uniquely, rather than merely predicting outcomes. Methods include graphical criteria such as do-calculus, structural equation models with assumptions about noise or functional form, and information from proxies, instruments, multiple environments, or targeted interventions. When unique recovery is impossible, partial-identification methods derive bounds, while newer work also examines finite-sample reliability and computational feasibility. A central challenge is that conclusions depend on assumptions and available data: stronger assumptions can make identification possible, but must be justified and tested against alternative models.

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128 papers20 papers in the last four weeks, up 54% on the four weeks before. 0.1% of all new papers.

Sharp Bounds on Continuous Treatment Effects under Unobserved Confounding
Oct 23, 2026Junzhe ZhangCausal Effect Estimation Causal Inference with Unobserved Confounding
Syracuse University
Identifiability from Stationary Covariance in Linear SDEs with Reciprocal Interactions and Latent Variables
Oct 22, 2026Ana Leticia Garcez Vicente, Saber SalehkaleybarCausal Identifiability Stochastic Differential Equations
Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Netherlands
Unified Sharp Bounds on Probabilities of Causation
Oct 19, 2026Xin Shu, Ang LiCausal Inference Partial Identification
Department of Computer Science, Florida State University
Causal Discovery beyond Conditional Independence: Identifiability, Algorithms, and Statistical Inference for Directed Acyclic and Cyclic Graphs
Oct 16, 2026Yang NiCausal Discovery Causal Identifiability
Department of Statistics and Data Science, The University of Texas at Austin
Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings
Oct 13, 2026Matteo Ceriscioli, Shohei Shimizu, Mallika Gupta +1Causal Discovery Causal Identifiability
Oregon State University, Causal Intelligence and Reasoning Lab, Corvallis, USA · RIKEN Advanced Intelligence Project, Causal Inference Team, Tokyo, Japan · The University of Osaka, Reasoning for Intelligence Department, Osaka, Japan +1
When Do Learned Blocks Preserve Causal Meaning? Block Identifiability and Constructive Causal Abstractions
Oct 13, 2026Karim Zaghw, Wieland Brendel, Patrik ReizingerCausal Representation Learning Causal Abstraction
University of Tübingen · ELLIS Institute Tübingen · Max Planck Institute for Intelligent Systems, Tübingen, Germany
Causal Effect Identifiability in Causal Abstractions: A Strict Hierarchy of Criteria
Oct 9, 2026Clément Yvernes, Marianne Clausel, Emilie Devijver +1Causal Identifiability Causal Abstraction
Univ Grenoble Alpes, CNRS, Grenoble INP, LIG · Université de Lorraine, CNRS, CRAN, 54000 Nancy
CausalBind: Causal Modeling and Learning for Protein-Molecule Virtual Screening
Oct 5, 2026Loka Li, Jin Tian, Kun ZhangCausal Representation Learning Structured Sparsity
Mohamed bin Zayed University of Artificial Intelligence · Carnegie Mellon University
Block Disentanglement in CRL: Bridging Identifiability and Visual State Estimation
Oct 5, 2026Emre Acartürk, Pranamya Kulkarni, Puranjay Datta +3Causal Representation Learning Causal Identifiability
Rensselaer Polytechnic Institute Troy, NY, USA · Swiss AI, EPFL Lausanne, Switzerland · University of Delaware Newark, DE, USA
Reasoning Models Are Accurate but Unsound on Identification
Oct 2, 2026Arman Behnam, Binghui WangQuery Identifiability Causal Identifiability
Department of Computer Science Illinois Institute of Technology Chicago, IL, USA
Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity
Oct 2, 2026Tatsuya Yamada, Hiroshi Morioka, Yoshinobu KawaharaCausal Representation Learning Time-Series Causal Discovery
The University of Osaka · RIKEN · Shiga University
Invariance of Clustering Operations in Causal Effect Identification
Oct 2, 2026Jani Nykänen, Otto Tabell, Santtu Tikka +1Causal Identifiability Causal Effect Estimation
Department of Mathematics and Statistics FI-40014 University of Jyvaskyla, Finland
Structure-agnostic Causal Representation Learning
Oct 1, 2026Arman Behnam, Binghui WangCausal Representation Learning OOD Generalization
Department of Computer Science Illinois Institute of Technology Chicago, IL, USA
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
Discrete Score Matching Enables Causal Discovery from Count Data
Sep 30, 2026Euijong Song, Hyewon Park, Gunwoong ParkScore Matching Causal Discovery
Department of Statistics Seoul National University, Korea · Interdisciplinary Program in Artificial Intelligence; Institute for Data Innovation in Science, Seoul National University, Korea
Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders
Sep 29, 2026Sadegh Khorasani, Ali Najar, Saber Salehkaleybar +1Structural Causal Models Causal Discovery
School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland · Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands · College of Management of Technology, EPFL, Lausanne, Switzerland
Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systems
Sep 29, 2026Julien Boussard, Antoine Debouchage, Théo SaulusStochastic Differential Equations Causal Identifiability
School of Computer Science, McGill University, Canada · Mila - Quebec AI Institute, Canada · LaMMe, Université Évry Paris-Saclay, France +1
Learning Causal Normalizing Flows from Incomplete Data via Observed-Data Likelihood
Sep 29, 2026Trung-Dung Hoang, Alceu Bissoto, Tim Flühmann +4Normalizing Flows Learning with Missing Data
Department of Digital Medicine, University of Bern, Switzerland · Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism (UDEM), Inselspital, Bern University Hospital, University of Bern, Switzerland · Graduate School for Cellular and Biomedical Sciences (GCB), University of Bern, Switzerland +3
Demistifying Data and Simulator Assumptions in Supervised Causal Discovery
Sep 29, 2026Pingchuan Ma, Rui Ding, Bojun Huang +1Causal Discovery Causal Structure Learning
Zhejiang University of Technology, China · Microsoft Research Asia · Sony Research +1
What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Views
Sep 29, 2026Heejin Jung, Gyeongdeok Seo, Hoyoon Byun +2Causal Effect Estimation Causal Foundation Models
Yonsei University · University of Illinois Urbana–Champaign
Self-Reports Do Not Identify Self-Models: An Identifiability Test for Counterfactual Reports
Sep 26, 2026Phongsakon Mark Konrad, Toygar Tanyel, Serkan AyvazLanguage Model Self-Assessment Metacognition in Language Models
Centre for Industrial Software, University of South- ern Denmark, Sønderborg, Denmark · Promake, Newark, DE, USA.
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
I Act Therefore I Am: When Is JEPA's Action-Conditioning Enough to Learn Causal Mechanisms?
Sep 25, 2026Yuhang Liu, Zhuo Huang, Javen Qinfeng ShiJoint-Embedding Predictive Architecture Causal Representation Learning
Responsible AI Research Centre, Australia
Evidence-Grounded Auditing of Identification Assumptions in Climate-Policy Causal Evaluations
Sep 25, 2026Yonghong Zhang, Yong Xie, Isabel M. Parra +1Causal Inference Policy Evaluation
Department of Finance, Universidad Autónoma de Madrid, Madrid, Spain · Spanish National Research Council (CSIC), Madrid, Spain
Decoupled Causal Discovery
Sep 20, 2026Zhengkang Guan, Fei Wu, Kun KuangCausal Discovery Causal Structure Learning
College of Computer Science and Technology Zhejiang University
Causal Inference with Unobserved Confounding: A Mixture Learning Perspective
Sep 19, 2026Mansi Sood, Devavrat ShahCausal Inference with Unobserved Confounding Causal Inference
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Sep 17, 2026Sambit Mishra, Yingying Wang, Christine K. Johnson +1Structural Causal Models Causal Discovery
University of Southern California · University of California, Davis
One Intervention per Component is Enough: Towards Identifiability in Linear Stochastic Dynamics from Steady State
Sep 17, 2026Saber SalehkaleybarParameter Estimation Stochastic Differential Equations
Leiden Institute of Advanced Computer Science, Leiden University, The Netherlands
On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models
Sep 16, 2026Sambit Mishra, Urbashi MitraStructural Causal Models Causal Discovery
Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles, CA 90089, USA
Causal Discovery via Transformed Low-Rank Quantile Surfaces
Sep 15, 2026Ryo Kamimura, Thong PhamCausal Discovery Causal Identifiability
Shiga University · RIKEN AIP · The University of Osaka

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Sharp Bounds on Continuous Treatment Effects under Unobserved Confounding

Junzhe Zhang

Syracuse University

Causal Effect Estimation Causal Inference with Unobserved Confounding Conditional Average Treatment Effect Estimation Causal Identifiability

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We study partial identification of conditional treatment effects with a continuous treatment under unobserved confounding. We target the conditional average potential outcome (CAPO) E[Y_x|z] and the conditional average treatment effect (CATE) between two treatment levels. We assume a bounded outcome and Lipschitz continuity of the response functions and of the propensity scores given the latent confounder, and show that these conditions imply the smooth treatment response and smooth treatment selection conditions of Kim et al. (2018). The combined bound of Kim et al. uses only two reference points on the identified diagonal and is generally loose. Bounding through every diagonal reference point instead gives explicit bounds whose average is the sharp identified set of the CAPO at every treatment, inside or outside the observational support. Capping the difference between the upper and lower bounds gives sharp causal bounds on the CATE. These bounds are informative without monotonicity or overlap assumptions.