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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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
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
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
Decoupled Causal Discovery
Sep 20, 2026Zhengkang Guan, Fei Wu, Kun KuangCausal Discovery Causal Structure Learning
College of Computer Science and Technology Zhejiang University
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
1Shiga University · 3RIKEN AIP · 2The University of Osaka
Symmetries and Causality: Causal Effect Identification Beyond IID Data
Sep 3, 2026Martin Rabel, Jakob RungeCausal Identifiability Causal Inference
Department of Computer Science, University of Potsdam, Potsdam, Germany
Guide, Not Bind: Why Defeasible Priors Fail in Augmented Lagrangian Causal Discovery
Sep 3, 2026Sairam Sundararaman, Sara Girdhar, Manit Narasimha Murthy +2Causal Discovery Causal Identifiability
Department of Computer Science (AI & ML) PES University · Department of Computer Science2026 PES University · Department of Computer Science PES University
Across-Design Uncertainty in Short Pricing Panels: Inference and Identification
Aug 21, 2026Pedro Cadahia DelgadoCausal Effect Estimation Uncertainty Quantification
PhD, Universidad de Huelva, Spain
Beyond Local Accuracy: A Protocol-Level Identifiability Audit for Controlled LLM Reasoning Evaluation
Aug 13, 2026Junhao Luo, Ning Huang, Ziqi Sha +2LLM Evaluation LLM Auditing
School of Statistics and Data Science, Southwestern University of Finance and Economics · School of Statistics and Data Science, Southwestern University of Finance and Economics Chengdu, China
General Probabilities of Causation with Causal Knowledge
Aug 12, 2026Xin Shu, Zhen Lei, Ang LiPartial Identification Causal Identifiability
Department of Computer Science, Florida State University
Operationalising Relative Causal Knowledge: Backbone Identifiability from Private Reports on a Shared Outcome
Aug 11, 2026Fabrizio Russo, Mark SomersStructural Causal Models Causal Abstraction
Department of Computing, Imperial College London · London, UK · Fifty One Degrees Ltd
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
Adversarial Causal Intervention Falsification
Aug 5, 2026Mojtaba EslamiStructural Causal Models Causal Discovery
University of Calgary
Causal Inference with Unstructured Outcomes
Aug 4, 2026Kevin Christian Wibisono, Yixin WangCausal Effect Estimation Causal Identifiability
Department of Statistics University of Michigan, Ann Arbor
Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference
Aug 2, 2026Omar Faruque, Pavan Raj Ravi, Jianwu WangCausal Effect Estimation Causal Identifiability
University of Maryland, Baltimore County
Why Does the Future Branch? Identifiable Closure Tests for Stochastic Physical World Models
Aug 1, 2026Yibin DongUncertainty Quantification World Models
Shandong University
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
Learning Bidirectional Causal Interactions with Heteroscedastic Neural Networks
Jul 24, 2026Masahiro TanakaCausal Effect Estimation Structural Causal Models
Faculty of Economics, Fukuoka University, Fukuoka, Japan
Equilibrium Causal Digital Twins: Validation, Transport, and Identification Limits
Jul 23, 2026Faraz Dadgostari, Neda NazemiCounterfactual Evaluation Structural Causal Models
Department of Mechanical & Industrial Engineering, Montana State University · Gianforte School of Computing, Montana State University
Equilibrium Causal Games: Separation, Identification, and the Identifiability of Cyclic Latent States
Jul 21, 2026Faraz Dadgostari, Neda NazemiCausal Representation Learning Causal Identifiability
Department of Mechanical & Industrial Engineering, Montana State University · Gianforte School of Computing, Montana State University
Verifying formulas for interventional distributions
Jul 15, 2026Francesco Freni, Leonard Henckel, Sebastian WeichwaldStructural Causal Models Causal Identifiability
Department of Mathematical Sciences, University of Copenhagen, Denmark · School of Mathematics and Statistics, University College Dublin, Ireland

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CausalBind: Causal Modeling and Learning for Protein-Molecule Virtual Screening

Loka Li, Jin Tian, Kun Zhang

Mohamed bin Zayed University of Artificial Intelligence · Carnegie Mellon University

Causal Representation Learning Structured Sparsity Structure-Based Drug Design Causal Identifiability Molecular Representation Learning

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Protein-molecule virtual screening is increasingly cast as a problem of representation learning in a shared embedding space. Existing methods rely on dense holistic alignment, entangling invariant binding determinants with nuisance correlations and limiting transfer to new targets. It has been noted that binding in protein-molecule systems involves sparse cross-modality interactions: binding is governed by a small contact interface and a few decisive local interactions (e.g., hydrogen bonds, hydrophobic contacts, and salt bridges) rather than the global structures of the protein and molecule. We hypothesize that uncovering and leveraging sparse interaction patterns is critical for generalization beyond the training data, as these patterns are reusable and expected to improve performance across different scenarios. In this paper, we aim to identify and leverage sparse interaction patterns, and verify our hypothesis. Since the training data contain only observed binding pairs, we formalize this prior via a V-structure causal model under Heckman-style selection, and establish three theoretical results: (i) the latent concepts of interacting proteins and molecules are not identifiable without appropriate sparsity constraints; (ii) these concepts and their sparse interactions are component-wise identifiable under structural sparsity conditions; and (iii) a low-rank relaxation of these conditions yields subspace identifiability of the concepts and interactions. Inspired by these principles, we propose CausalBind with three implementation variants. Extensive experiments on DUD-E and LIT-PCBA benchmarks show that all variants consistently outperform strong retrieval baselines, with the largest gains on LIT-PCBA early enrichment, and further generalize to target- and scaffold-level out-of-distribution splits. Code is available at https://github.com/lokali/CausalBind.

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