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.

Latest papers

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

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
Optimal Experiments for Partial Causal Effect Identification
May 7, 2026Tobias Maringgele, Jalal EtesamiCausal Effect Estimation Partial Identification
Technical University of Munich
A Topological Sorting Criterion for Random Causal Directed Acyclic Graphs
May 7, 2026Alexander G. Reisach, Antoine Chambaz, Gilles Blanchard +1Causal Discovery Causal Identifiability
Laboratoire de Mathématiques d’Orsay Université Paris-Saclay 91400 Orsay, France · CNRS, MAP5 Université Paris Cité F-75006 Paris, France · Department of Mathematical Sciences and Pioneer Centre for Artificial Intelligence, University of Copenhagen, Denmark
MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series
May 6, 2026Shicheng Fan, Nour Elhendawy, Jianle Sun +4Causal Representation Learning Causal Identifiability
University of Illinois Chicago · Case Western Reserve University · Carnegie Mellon University +1
Causal discovery under mean independence and linearity
May 6, 2026Geert Mesters, Alvaro Ribot, Anna Seigal +1Structural Causal Models Causal Discovery
Partially Observed Structural Causal Models
May 5, 2026Turan Orujlu, Jordan Matelsky, Martin V. Butz +2Structural Causal Models Causal Identifiability
University of Tübingen · MPI for Biological Cybernetics · University of Pennsylvania +1
Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time
Apr 28, 2026Jennifer Wendland, Nicolas Freitag, Maik KschischoCausal Effect Estimation Clinical Outcome Prediction
Department of Computer Science University of Koblenz 53424 Koblenz, Germany
Causal Representation Learning from General Environments under Nonparametric Mixing
Apr 26, 2026Ignavier Ng, Shaoan Xie, Xinshuai Dong +2Causal Representation Learning Causal Discovery
1Carnegie Mellon University · 2Mohamed bin Zayed University of Artificial Intelligence
Efficient Symbolic Computations for Identifying Causal Effects
Apr 22, 2026Benjamin Hollering, Pratik Misra, Nils SturmaStructural Causal Models Symbolic Reasoning
Max Planck Institute for Mathematics in the Sciences, Germany · Binghamton University, USA · École Polytechnique Fédérale de Lausanne, Switzerland
Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing
Apr 14, 2026Danru Xu, Sébastien Lachapelle, Sara MagliacaneCausal Representation Learning Representation Identifiability
University of Amsterdam · Samsung AI Lab, Montreal · Saarland University & University of Amsterdam
Iterative Identification Closure: Amplifying Causal Identifiability in Linear SEMs
Apr 10, 2026Ziyi Ding, Xiao-Ping ZhangCausal Identifiability Structural Causal Models
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
Identifying Causal Effects Using a Single Proxy Variable
Apr 10, 2026Silvan Vollmer, Niklas Pfister, Sebastian WeichwaldCausal Effect Estimation Causal Inference with Unobserved Confounding
Department of Mathematical Sciences University of Copenhagen Copenhagen, Denmark · Lakera AI Zurich, Switzerland
The Digital Twin Counterfactual Framework: A Validation Architecture for Simulated Potential Outcomes
Apr 1, 2026Olav LaudyCounterfactual Evaluation Causal Effect Estimation
Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions
Mar 23, 2026Panayiotis Panayiotou, Özgür ŞimşekCausal Discovery Causal Identifiability
Department of Computer Science, University of Bath, UK
Beyond Means: Topological Causal Effects under Persistent-Homology Ignorability
Mar 15, 2026Amir Saki, Usef FaghihiCausal Effect Estimation Causal Identifiability
Université du Québec à Trois-Rivières (UQTR).
Proxy-Guided Measurement Calibration
Mar 10, 2026Saketh Vishnubhatla, Shu Wan, Andre Harrison +2Proximal Causal Inference Causal Inference
Arizona State University, Tempe, AZ · DEVCOM Army Research Lab, Adelphi, MD
Structural Causal Bottleneck Models
Mar 9, 2026Simon Bing, Jonas Wahl, Jakob RungeStructural Causal Models Causal Effect Estimation
Equal contribution · Technische Universität Berlin · Department of Computer Science, University of Potsdam +1
Sign Identifiability of Causal Effects in Stationary Stochastic Dynamical Systems
Mar 9, 2026Gijs van Seeventer, Saber SalehkaleybarStochastic Differential Equations Structural Causal Models
1LIACS, Leiden University, Netherlands
Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning
Mar 5, 2026Haoyue Dai, Immanuel Albrecht, Peter Spirtes +1Causal Discovery Causal Discovery with Latent Confounding
Carnegie Mellon University · FernUniversität in Hagen · MBZUAI
Scalable Contrastive Causal Discovery under Unknown Soft Interventions
Mar 3, 2026Mingxuan Zhang, Khushi Desai, Sopho Kevlishvili +1Causal Discovery Structural Causal Models
Department of Systems Biology, Columbia University · Irving Institute for Cancer Dynamics, Columbia University · Department of Computer Science, Columbia University +2
SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry
Mar 1, 2026Rong Fu, Chunlei Meng, Jinshuo Liu +8Uncertainty Decomposition Uncertainty Calibration
Independent Researcher
Robust Weighted Triangulation of Causal Effects Under Model Uncertainty
Mar 1, 2026Rohit Bhattacharya, Ina Ocelli, Ted WestlingSemiparametric Inference Causal Effect Estimation
Dept. of Computer Science, Williams College · Dept. of Mathematics and Statistics, University of Massachusetts Amherst
Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data
Feb 27, 2026Yoichi ChikaharaCausal Discovery Causal Identifiability
Communication Science Laboratories, NTT, Inc., Kyoto, Japan
Causal Identification from Counterfactual Data: Completeness and Bounding Results
Feb 26, 2026Arvind Raghavan, Elias BareinboimCausal Inference Causal Identifiability
Causal Artificial Intelligence Lab, Department of Computer Science, Columbia University
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
Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
Feb 23, 2026Shimeng Huang, Matthew Robinson, Francesco LocatelloInstrumental Variable Estimation Causal Identifiability
Institute of Science and Technology Austria (ISTA)
Bounds and Identification of Joint Probabilities of Potential Outcomes and Observed Variables under Monotonicity Assumptions
Feb 21, 2026Naoya Hashimoto, Yuta Kawakami, Jin TianMonotonicity Constraints Causal Identifiability
Mohamed bin Zayed University of Artificial Intelligence
Bounding Probabilities of Causation with Partial Causal Diagrams
Feb 16, 2026Yuxuan Xie, Ang LiCausal Inference Causal Identifiability
University of Ottawa, Ontario, Canada · Department of Computer Science, Florida State University, FL, U.S.A.
Uncertainty-aware Causal Decision Making via Effect Bound Decomposition
Jan 30, 2026Md Musfiqur Rahman, Ziwei Jiang, Hilaf Hasson +1Causal Effect Estimation Parameter Identifiability
Electrical and Computer Engineering, Purdue University · Computer Science, Johns Hopkins University · Cohesity
TRACE: Trajectory Recovery for Continuous Mechanism Evolution in Causal Representation Learning
Jan 29, 2026Shicheng Fan, Kun Zhang, Lu ChengCausal Representation Learning Causal Identifiability
University of Illinois at Chicago · Carnegie Mellon University · Mohamed bin Zayed University of Artificial Intelligence

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Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks

Gandharv Patil, Keyi Tang, Raquel Aoki, Leo Guelman

McGill University/Mila · RBC Borealis

Causal Effect Estimation Causal Identifiability Causal Reasoning Individualized Treatment Effect Estimation

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Individual treatment effects are not point-identified from data. The Probability of Necessity and Sufficiency (PNS) circumvents this limitation by characterizing individual-level causality through intersection bounds derived from combined experimental and observational data. In finite samples, however, standard plug-in estimators systematically fail: they violate structural probability constraints and suffer from extremum bias induced by max-min operators, yielding spuriously narrow intervals. We propose a neural framework for finite-sample PNS estimation that resolves both pathologies. We introduce an anchored neural architecture that guarantees structural constraint satisfaction by construction. To correct extremum bias, we employ precision-corrected intersection-bound inference, leveraging Epistemic Neural Networks for scalable, high-dimensional uncertainty quantification. Empirical evaluations confirm that this approach maintains nominal coverage and exact constraint validity in high-dimensional regimes where standard estimators systematically undercover.