Causal Discovery

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Causal discovery studies how to recover directed relationships among variables when experiments are limited or observational data alone are ambiguous. Methods include conditional-independence tests, score-based and ordering searches, functional models such as additive-noise and LiNGAM, and approaches that use interventions or changes across environments; time-series methods also model lagged effects and feedback. Latent confounding, missing data, nonstationarity, and measurement noise complicate recovery, so results often depend on assumptions and may identify only an equivalence class rather than one graph. Current work seeks methods that scale to large and diverse datasets while quantifying uncertainty and testing robustness, with a continuing challenge of translating benchmark performance into reliable real-world discoveries.

Latest papers

222 papers31 papers in the last four weeks, up 72% on the four weeks before. 0.2% of all new papers.

DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data
Feb 1, 2026Muhammad Hasan Ferdous, Ian Stockwell, Md Osman GaniTime-Series Causal Discovery Causal Discovery
Causal AI Lab, Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA
Skewness-Robust Causal Discovery in Location-Scale Noise Models
Nov 18, 2025Daniel Klippert, Alexander MarxCausal Discovery
Department of Statistics, TU Dortmund University, Germany · Research Center Trustworthy Data Science and Security, University Alliance Ruhr, Germany.
MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventions
Oct 25, 2025Hans Jarett Ong, Yoichi Chikahara, Tomoharu IwataMeta-Learning Causal Discovery
Nara Institute of Science and Technology, Nara, Japan · NTT Communication Science Laboratories, Kyoto, Japan
Learning Granger Causality under Latent Confounding via Intervention-Induced Heterogeneity
Oct 22, 2025Ziyi Zhang, Shaogang Ren, Xiaoning Qian +1Time-Series Causal Discovery Causal Discovery
Texas A&M University · University of Tennessee at Chattanooga · Brookhaven National Laboratory
Design of Experiment for Discovering Directed Mixed Graph
Sep 2, 2025Haijie Xu, Chen ZhangGraph Structure Learning Causal Discovery
Department of Industrial Engineering Tsinghua University
Tags for DAGs: Graph Refinement with Meta-Informed Relations
Jun 24, 2025Florian Peter Busch, Moritz Willig, Florian Guldan +2Causal Discovery Causal Structure Learning
Computer Science Department, Technical University of Darmstadt · Hessian Center for AI (hessian.AI) · German Research Center for AI (DFKI) +2
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 · Carnegie Mellon University, Pittsburgh PA, USA · ENSAE, CREST, IP Paris, France +2
The observational partial order of causal structures with latent variables
Feb 11, 2025Marina Maciel Ansanelli, Elie Wolfe, Robert W. SpekkensLatent Variable Models Causal Discovery
Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo, Ontario Canada N2L 2Y5 · Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1
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
CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
Apr 9, 2024Yu Zhou, Xingyu Wu, Jibin Wu +2LLM Evaluation Causal Discovery
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR 999077, China · College of Computer Science, Chongqing University, Chongqing 400044, China
Discovering and Reasoning of Causality in the Hidden World with Large Language Models
Feb 6, 2024Chenxi Liu, Yongqiang Chen, Tongliang Liu +4Causal Discovery Causal Inference
TMLR Group, Hong Kong Baptist University, Kowloon, Hong Kong SAR · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA 15213, USA +3
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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DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data

Muhammad Hasan Ferdous, Ian Stockwell, Md Osman Gani

Causal AI Lab, Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA

Time-Series Causal Discovery Causal Discovery Non-Stationary Time Series

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Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-term fluctuations, complicating causal inference under non-stationarity and autocorrelation. Existing causal discovery methods typically operate on raw observations, making them vulnerable to spurious edges and misattributed temporal dependencies. We introduce a decomposition-based causal discovery framework that separates each time series into trend, seasonal, and residual components and performs component-specific causal analysis. Trend components are assessed using stationarity tests, seasonal components using kernel-based dependence measures, and residual components using constraint-based causal discovery. The resulting component-level graphs are integrated into a unified multi-scale causal structure. This approach isolates long- and short-range causal effects, reduces spurious associations, and improves interpretability. Across extensive synthetic benchmarks and real-world climate data, our framework more accurately recovers ground-truth causal structure than state-of-the-art baselines, particularly under strong non-stationarity and temporal autocorrelation.