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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Towards a holistic understanding of Selection Bias for Causal Effect Identification
May 13, 2026Yiwen Qiu, Filip Kovačević, Shimeng Huang +2Causal Effect Estimation Data Selection
Institute of Science and Technology Austria (ISTA) · Carnegie Mellon University
IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning
May 13, 2026Vahid Balazadeh, Hamidreza Kamkari, Medha Barath +2Causal Effect Estimation Parameter Identifiability
University of Toronto · Vector Institute · MIT CSAIL +1
Coarsening Linear Non-Gaussian Causal Models with Cycles
May 11, 2026Francisco Madaleno, Francisco C Pereira, Alex MarkhamStructural Causal Models Causal Discovery
Department of Technology, Management and Economics, Technical University of Denmark · Department of Mathematical Sciences,11 University of Copenhagen
Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions
May 11, 2026Tsuyoshi OkitaTime-Series Causal Discovery Causal Discovery
Kyushu Institute of Technology
CIVeX: Causal Intervention Verification for Language Agents
May 9, 2026Fabio RovaiStructural Causal Models Tool-Using Agents
The Tesseract Academy
Position: Mechanistic Interpretability Must Disclose Identification Assumptions for Causal Claims
May 8, 2026Zezheng Lin, Fengming LiuMechanistic Interpretability Causal Identifiability
Preprint.
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 · UC Berkeley
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
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
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
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
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
Clustering and Pruning in Causal Data Fusion
May 21, 2025Otto Tabell, Santtu Tikka, Juha KarvanenClustering Causal Identifiability
Department of Mathematics and Statistics FI-40014 University of Jyvaskyla, Finland
Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
Feb 27, 2025Ambroise Heurtebise, Omar Chehab, Pierre Ablin +2Structural Causal Models Causal Discovery
Inria, CEA, University of Paris-Saclay, France · University of Carnegie Mellon, Pittsburgh PA, USA · Apple +1
Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption
Feb 27, 2025Weilin Chen, Ruichu Cai, Jie Qiao +2Causal Effect Estimation Causal Identifiability
School of Computer Science, Guangdong University of Technology, Guangzhou, 510006, China · School of Computer Science, Guangdong University of Technology, Guangzhou, 510006, China and Peng Cheng Laboratory, Shenzhen, China · Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
Identifiability Analysis of Linear ODE Systems with Hidden Confounders
Oct 29, 2024Yuanyuan Wang, Biwei Huang, Wei Huang +2Parameter Identifiability Causal Identifiability
The University of Melbourne · University of California, San Diego
Towards Complete Causal Explanation with Expert Knowledge
Jul 10, 2024Aparajithan Venkateswaran, Emilija PerkovićStructural Causal Models Causal Discovery
Microsoft · Redmond, WA 98052, USA · Department of Statistics +2
Causal Discovery in Mixtures of Populations
Nov 13, 2023Bijan Mazaheri, Spencer Gordon, Yuval Rabani +1Latent Variable Models Causal Discovery
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA · Fragment Data Technologies, San Francisco, California, USA · Department of Computer Science, Hebrew Univeristy of Jerusalem, Jerusalem, Israel +1

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Towards a holistic understanding of Selection Bias for Causal Effect Identification

Yiwen Qiu, Filip Kovačević, Shimeng Huang, Peter Spirtes+1

Institute of Science and Technology Austria (ISTA) · Carnegie Mellon University

Causal Effect Estimation Data Selection Causal Identifiability Selection Bias

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Selection bias is pervasive in observational studies. For example, large scale biobanks data can exhibit ``healthy volunteer bias'' when respondents are healthier and of higher socio-economic status than the population they are meant to represent. Recovering causal effects from such sub-population is an important problem in causal inference, as estimating average treatment effects (ATE) from selected populations can result in a severely biased estimate of the ATE from the whole population. In this paper, we investigate the identifiability of the ATE under selection bias. We provide necessary and sufficient conditions for ATE identifiability, leveraging weak assumptions on probability classes to characterize propensity score and selection probability. Compared to previous works, our results extend existing graphical identifiability criteria and offer a more comprehensive understanding of causal effect identification with strictly weaker conditions in the presence of selection bias.

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