Causal Discovery

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

5 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 294 papers with code (743 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• DAGs with NO TEARS: Continuous Optimization for Structure Learning
  4 Mar 2018 6 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  This is achieved by a novel characterization of acyclicity that is not only smooth but also exact.
• DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
  16 Sep 2022 5 repositories listed Syntology ran 3 of 7 samples · 4 unverified · 2 pointer-only (licence)
  From the optimization side, we drop the typically used augmented Lagrangian scheme and propose DAGMA (DAGs via M-matrices for Acyclicity), a method that resembles the central path for barrier methods.
• Testing Conditional Independence in Supervised Learning Algorithms
  28 Jan 2019 4 repositories listed
  We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of the association between one or several features and a given outcome, conditional on a reduced feature set.
• A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
  27 Mar 2023 3 repositories listed
  The ability to understand causality from data is one of the major milestones of human-level intelligence.
• Federated Causal Discovery From Interventions
  7 Nov 2022 3 repositories listed Syntology ran 0 of 13 samples · 13 unverified
  We propose FedCDI, a federated framework for inferring causal structures from distributed data containing interventional samples.
• Estimating Transfer Entropy via Copula Entropy
  10 Oct 2019 3 repositories listed
  Causal discovery is a fundamental problem in statistics and has wide applications in different fields.
• Causal Discovery Toolbox: Uncover causal relationships in Python
  6 Mar 2019 3 repositories listed Syntology ran 5 of 19 samples · 14 unverified
  This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling.
• Causal Evaluation of Language Models
  1 May 2024 2 repositories listed
  Recent advances in language models have expanded the horizons of artificial intelligence across various domains, sparking inquiries into their potential for causal reasoning.
• The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
  17 Apr 2024 2 repositories listed Syntology ran 3 of 29 samples · 26 unverified
  The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and measure an array of variables from these physical systems, providing a rich testbed for algorithms from a…
• Causal Discovery under Off-Target Interventions
  13 Feb 2024 2 repositories listed
  Causal graph discovery is a significant problem with applications across various disciplines.
• Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
  2 Feb 2024 2 repositories listed
  The careful and appropriate application of the proposed approach in this work, with improvement and customization for each domain, can thus address challenges such as dataset biases and limitations, illustrating the…
• Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice
  27 Oct 2023 2 repositories listed
  We find that, while the choice of algorithm remains crucial to obtaining state-of-the-art performance, hyperparameter selection in ensemble settings strongly influences the choice of algorithm, in that a poor choice of…
• Missing Data Imputation Based on Dynamically Adaptable Structural Equation Modeling with Self-Attention
  23 Aug 2023 2 repositories listed
  Addressing missing data in complex datasets including electronic health records (EHR) is critical for ensuring accurate analysis and decision-making in healthcare.
• Order-based Structure Learning with Normalizing Flows
  14 Aug 2023 2 repositories listed Syntology ran 4 of 5 samples · 1 unverified
  Estimating the causal structure of observational data is a challenging combinatorial search problem that scales super-exponentially with graph size.
• BISCUIT: Causal Representation Learning from Binary Interactions
  16 Jun 2023 2 repositories listed
  Identifying the causal variables of an environment and how to intervene on them is of core value in applications such as robotics and embodied AI.
• causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge Graphs
  20 Jan 2023 2 repositories listed
  This paper describes a novel Python package, named causalgraph, for modeling and saving causal graphs embedded in knowledge graphs.
• GFlowCausal: Generative Flow Networks for Causal Discovery
  15 Oct 2022 2 repositories listed
  Causal discovery aims to uncover causal structure among a set of variables.
• gCastle: A Python Toolbox for Causal Discovery
  30 Nov 2021 2 repositories listed
  gCastle is an end-to-end Python toolbox for causal structure learning.
• Learning Temporally Causal Latent Processes from General Temporal Data
  11 Oct 2021 2 repositories listed Syntology ran 1 of 6 samples · 5 unverified
  In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be…
• Learning Temporally Latent Causal Processes from General Temporal Data
  29 Sep 2021 2 repositories listed
  Our goal is to find time-delayed latent causal variables and identify their relations from temporal measured variables.
• Efficient Neural Causal Discovery without Acyclicity Constraints
  22 Jul 2021 2 repositories listed
  Learning the structure of a causal graphical model using both observational and interventional data is a fundamental problem in many scientific fields.
• DiBS: Differentiable Bayesian Structure Learning
  25 May 2021 2 repositories listed Syntology ran 3 of 4 samples · 1 unverified
  In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation.
• Neural graphical modelling in continuous-time: consistency guarantees and algorithms
  6 May 2021 2 repositories listed Syntology ran 2 of 9 samples · 7 unverified
  In this paper, we consider score-based structure learning for the study of dynamical systems.
• Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game
  26 Feb 2021 2 repositories listed Syntology ran 3 of 5 samples · 2 unverified
  Here, we show that marginal variance tends to increase along the causal order for generically sampled additive noise models.
• Causal Autoregressive Flows
  4 Nov 2020 2 repositories listed
  We exploit the fact that autoregressive flow architectures define an ordering over variables, analogous to a causal ordering, to show that they are well-suited to performing a range of causal inference tasks, ranging…
• Autoregressive flow-based causal discovery and inference
  18 Jul 2020 2 repositories listed Syntology ran 11 of 15 samples · 4 unverified
  We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual predictions.
• Masked Gradient-Based Causal Structure Learning
  18 Oct 2019 2 repositories listed
  This paper studies the problem of learning causal structures from observational data.
• Learning Sparse Nonparametric DAGs
  29 Sep 2019 2 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data.
• Causal Discovery with Cascade Nonlinear Additive Noise Models
  23 May 2019 2 repositories listed
  In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and…
• Discovering Causal Signals in Images
  26 May 2016 2 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Our experiments demonstrate the existence of a relation between the direction of causality and the difference between objects and their contexts, and by the same token, the existence of observable signals that reveal…

Syntology lines on 13 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.