Causal Identifiability

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11 papers in the last four weeks, up 83% on the four weeks before. 0.1% of all new papers.

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Relaxing Faithfulness with Intervention-Only Causal Discovery
Jul 13, 2026Bijan Mazaheri, Jiaqi Zhang, Caroline UhlerCausal Discovery Causal Identifiability
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA · Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA · Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Structure Learning on Clustered Data
Jul 9, 2026Ryan Thompson, Matt P. Wand, Veerabhadran BaladandayuthapaniCausal Structure Learning Causal Identifiability
School of Mathematical and Physical Sciences University of Technology Sydney Ultimo, NSW 2007, Australia · Department of Biostatistics University of Michigan Ann Arbor, MI 48105, USA
Geometric Causal Models
Jul 6, 2026Eli N. Weinstein, David M. BleiCausal Effect Estimation Causal Identifiability
Department of Chemistry, Technical University of Denmark, Kgs. Lyngby, DK. · Departments of Statistics and Computer Science, Columbia University, New York, NY, USA.
Knowledge-Informed Local Causal Discovery of Optimal Adjustment Sets
Jul 5, 2026Seong Woo Ahn, Alessandro Leite, José Lucas De Melo Costa +3Causal Effect Estimation Causal Discovery
Laboratoire Interdisciplinaire des Sciences du Numérique, Université Paris-Saclay, CNRS, CentraleSupélec, France · LITIS UR 4108, INSA Rouen Normandy, University of Rouen Normandy, France
Hierarchical Clustering As a Novel Solution to the Notorious Multicollinearity Problem in Observational Causal Inference
Jun 30, 2026Yufei Wu, Zhiying Gu, Alex Deng +2Causal Identifiability Causal Inference
Airbnb, Inc. San Francisco, California, USA
Non-parametric recovery of causal diffusion mechanisms from steady-state observations
Jun 29, 2026Richard Schwank, Mathias DrtonStochastic Differential Equations Causal Identifiability
School of Computation, Information and Technology, Technical University of Munich · 2Munich Center for Machine Learning
Disentangling Continuous-Time Latent Dynamics: Identifiability of Latent SDEs via Diffusion Shifts
Jun 26, 2026Yuanyuan Wang, Wenjie Wang, Haoxuan Li +2Stochastic Differential Equations Causal Representation Learning
Mohamed bin Zayed University of Artificial Intelligence · The University of Melbourne · Peking University +1
Causal Gaussian Processes for Robust Treatment Effect Evaluation with Unobserved Confounding
Jun 20, 2026Junzhe Zhang, Jingyuan Chen, Elias BareinboimCausal Effect Estimation Policy Evaluation
Department of Electrical Engineering and Computer Science, Syracuse University · Department of Computer Science, Columbia University
Temporal Causal Prior-Data Fitted Networks for Panel Data with Learned Reliability Signals
Jun 18, 2026Shravan Talupula, Saurabh SharmaCausal Foundation Models Causal Effect Estimation
ProfitOps Inc.
Unsupervised Causal Abstractions Discovery
Jun 17, 2026Théo Saulus, Simon Lacoste-Julien, Dhanya SridharStructural Causal Models Causal Discovery
Mila - Quebec AI Institute · Université de Montréal · Canada CIFAR AI Chair
Identification and Inference for Algorithmic Frontiers with Selective Labels
Jun 12, 2026Yiqi Liu, Francesca Molinari, Amilcar VelezDebiased ML Parameter Identifiability
Department of Economics, Cornell University
Relational Structural Causal Models
Jun 12, 2026Adiba Ejaz, Elias BareinboimStructural Causal Models Causal Identifiability
Causal AI Lab, Department of Computer Science, Columbia University, NY, USA.
WorldKernel: A World Model is the Coupling Kernel of Admissible Possible Worlds
Jun 9, 2026Fabio RovaiStructural Causal Models Causal World Models
Computational Identifiability
Jun 8, 2026Lucius E. J. Bynum, Rajesh Ranganath, Kyunghyun ChoCausal Effect Estimation Parameter Identifiability
New York University
Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variables
Jun 6, 2026Mariyam Khan, Shohei Shimizu, Thong PhamGraph Structure Learning Causal Discovery
University of Bergen · *Work conducted while a student trainee at RIKEN AIP · 2The University of Osaka +2
Causal Modeling of Selection in Evolution
Jun 4, 2026Haoyue Dai, Zeyu Tang, Peter Spirtes +1Structural Causal Models Causal Discovery
Carnegie Mellon University · Stanford University · Mohamed bin Zayed University of Artificial Intelligence
Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphs
Jun 2, 2026Clément Yvernes, Emilie Devijver, Marianne Clausel +1Causal Effect Estimation Causal Identifiability
Univ Grenoble Alpes, CNRS, Grenoble INP, LIG, France · Université de Lorraine, CNRS, CRAN, 54000 Nancy, France.
Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families
Jun 1, 2026Roel Hulsman, Carles Balsells-Rodas, Sara MagliacaneTime-Series Causal Discovery Regime-Switching Dynamical Systems
University of Amsterdam · 2Adyen · 3Imperial College London +1
Topological Ignorability for Structural Causal Effects Beyond Means
May 31, 2026Usef FaghihiCausal Effect Estimation Causal Identifiability
∗Université du Québec à Trois-Rivières (UQTR).
Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery
May 30, 2026Tyler H. McCormickCausal Identifiability Scientific ML
Departments of Statistics and Sociology, University of Washington, Seattle, WA, USA.
On the Recoverability of Causal Relations from Bulk Gene Expression Data
May 30, 2026Gongxu Luo, Boyang Sun, Kun ZhangStructural Causal Models Causal Discovery
Mohamed bin Zayed University of Artificial Intelligence · Carnegie Mellon University
Why LLMs Fail at Causal Discovery and How Interventional Agents Escape
May 26, 2026Amartya Roy, Sonali ParbhooCausal Discovery Causal Reasoning in Language Models
SIRE, IIT Delhi and Robert Bosch GmbH, India · Imperial College London
Iterative Causal Discovery: Per-Edge Impossibility Certificates, Tier-Aware Oracle Queries, and the $1+K$ Lower Bound
May 26, 2026Eichi UeharaBayesian Networks Causal Discovery
aflo, Inc., Tokyo, Japan
Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction
May 25, 2026Wenkang Jiang, Yuhang Liu, Erdun Gao +3Causal Representation Learning Latent Dynamics Modeling
Learning Causal Orderings for In-Context Tabular Prediction
May 21, 2026Sascha Xu, Sarah Mameche, Jilles VreekenIncomplete Data Imputation Causal Structure Learning
CISPA Helmholtz Center
Causal Discovery in Structural VAR Models Under Equal Noise Variance
May 21, 2026SeyedSina Seyedi HasanAbadi, Fahimeh Arab, Erfan Nozari +1Time-Series Causal Discovery Causal Discovery
Bourns College of Engineering, University of California, Riverside · University of California, San Francisco · Department of Mathematics and Statistics, Boston University
Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
May 20, 2026Zeyu Liu, Zheng Li, Feng Xie +3Causal Effect Estimation Causal Discovery
Department of Applied Statistics, Beijing Technology and Business University, Beijing, China · Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China +2
Score-Based Causal Discovery of Latent Variable Causal Models
May 19, 2026Ignavier Ng, Xinshuai Dong, Haoyue Dai +3Latent Variable Models Structural Causal Models
Carnegie Mellon University · University of California, San Diego · Mohamed bin Zayed University of Artificial Intelligence
Identifiable Multimodal Causal Representation Learning under Partial Latent Sharing
May 18, 2026Manal Benhamza, Marianne Clausel, Myriam TamiLatent Variable Models Causal Representation Learning
Paris-Saclay University, CentraleSupélec, MICS Lab, France · Lorraine University, CRAN, France
Causal Learning with the Invariance Principle
May 13, 2026Francesco Montagna, Francesco LocatelloStructural Causal Models Causal Discovery
Institute of Science and Technology Austria

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Relaxing Faithfulness with Intervention-Only Causal Discovery

Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler

Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA · Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA · Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

Causal Discovery Causal Identifiability Causal Intervention

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Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence properties on observational data to determine partially directed causal relationships, then applying interventions to orient the unknown causal directions. A critical assumption for the first step is faithfulness: a requirement that causally linked variables exhibit statistical dependence. Many natural systems include buffering and stabilizing pathways that cancel out to achieve systemic robustness. This cancellation of pathways violates faithfulness, leading causal discovery algorithms to incorrectly remove causal dependencies. In this paper, we argue that hard interventions contain information about the presence/absence of causal linkage that is overlooked in the first stage of structure discovery. We show that a mild assumption -- called intervention-immediacy faithfulness -- that allows cancellations, is sufficient to nonparametrically identify causal structures with hard interventions. These results position interventions as the primary carriers of information about causal structure, which should take precedence over conditional independence testing. To flip the paradigm, we also specify equivalence classes when the identification criteria are not met due to limitations in the scope of interventions.

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Latest papers 86