{"total":207,"rows":[{"id":"2103.02582","title":"D'ya like DAGs? A Survey on Structure Learning and Causal Discovery","created":"2021-03-03","cited_by_count":211,"area":null},{"id":"2102.13647","title":"Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game","created":"2021-02-26","cited_by_count":161,"area":null},{"id":"2209.08037","title":"DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization","created":"2022-09-16","cited_by_count":140,"area":null},{"id":"2105.11839","title":"DiBS: Differentiable Bayesian Structure Learning","created":"2021-05-25","cited_by_count":106,"area":null},{"id":"2306.00542","title":"Nonparametric Identifiability of Causal Representations from Unknown Interventions","created":"2023-06-01","cited_by_count":106,"area":null},{"id":"2202.02195","title":"Deep End-to-end Causal Inference","created":"2022-02-04","cited_by_count":95,"area":null},{"id":"2107.10483","title":"Efficient Neural Causal Discovery without Acyclicity Constraints","created":"2021-07-22","cited_by_count":82,"area":null},{"id":"2210.06201","title":"Diffusion Models for Causal Discovery via Topological Ordering","created":"2022-10-12","cited_by_count":66,"area":"diffusion"},{"id":"2111.15155","title":"gCastle: A Python Toolbox for Causal Discovery","created":"2021-11-30","cited_by_count":61,"area":null},{"id":"2105.06631","title":"Ordering-Based Causal Discovery with Reinforcement Learning","created":"2021-05-14","cited_by_count":55,"area":null},{"id":"2106.07197","title":"DAGs with No Curl: An Efficient DAG Structure Learning Approach","created":"2021-06-14","cited_by_count":53,"area":null},{"id":"2111.03187","title":"MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms","created":"2021-11-04","cited_by_count":51,"area":null},{"id":"2104.05441","title":"Unsuitability of NOTEARS for Causal Graph Discovery","created":"2021-04-12","cited_by_count":47,"area":null},{"id":"2206.07824","title":"Large-Scale Differentiable Causal Discovery of Factor Graphs","created":"2022-06-15","cited_by_count":47,"area":null},{"id":"2303.10112","title":"Causal Discovery from Temporal Data: An Overview and New Perspectives","created":"2023-03-17","cited_by_count":43,"area":null},{"id":"2109.02429","title":"Learning Neural Causal Models with Active Interventions","created":"2021-09-06","cited_by_count":40,"area":null},{"id":"2304.02146","title":"Structure Learning with Continuous Optimization: A Sober Look and Beyond","created":"2023-04-04","cited_by_count":36,"area":null},{"id":"2305.13341","title":"Discovering Causal Relations and Equations from Data","created":"2023-05-21","cited_by_count":36,"area":null},{"id":"2208.14153","title":"Identifying Weight-Variant Latent Causal Models","created":"2022-08-30","cited_by_count":34,"area":null},{"id":"2109.04286","title":"NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge","created":"2021-09-09","cited_by_count":33,"area":null},{"id":"2211.00216","title":"Distributed Graph Neural Network Training: A Survey","created":"2022-11-01","cited_by_count":32,"area":null},{"id":"2310.15709","title":"Causal Representation Learning Made Identifiable by Grouping of Observational Variables","created":"2023-10-24","cited_by_count":32,"area":null},{"id":"2110.09356","title":"Towards Federated Bayesian Network Structure Learning with Continuous Optimization","created":"2021-10-18","cited_by_count":31,"area":null},{"id":"2311.10263","title":"Stable Differentiable Causal Discovery","created":"2023-11-17","cited_by_count":31,"area":null},{"id":"2103.14232","title":"ACRE: Abstract Causal REasoning Beyond Covariation","created":"2021-03-26","cited_by_count":30,"area":null},{"id":"2210.17283","title":"CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data","created":"2022-10-31","cited_by_count":29,"area":null},{"id":"2310.13387","title":"Assumption violations in causal discovery and the robustness of score matching","created":"2023-10-20","cited_by_count":28,"area":null},{"id":"2206.01665","title":"BaCaDI: Bayesian Causal Discovery with Unknown Interventions","created":"2022-06-03","cited_by_count":26,"area":null},{"id":"2208.14571","title":"Truncated Matrix Power Iteration for Differentiable DAG Learning","created":"2022-08-30","cited_by_count":23,"area":null},{"id":"2104.08043","title":"Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data","created":"2021-04-16","cited_by_count":22,"area":null},{"id":"2205.13869","title":"MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models","created":"2022-05-27","cited_by_count":22,"area":null},{"id":"2307.09552","title":"Self-Compatibility: Evaluating Causal Discovery without Ground Truth","created":"2023-07-18","cited_by_count":22,"area":null},{"id":"2203.02336","title":"Differentiable Causal Discovery Under Latent Interventions","created":"2022-03-04","cited_by_count":21,"area":null},{"id":"2301.01849","title":"NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning","created":"2023-01-04","cited_by_count":20,"area":null},{"id":"2303.02186","title":"Causal Deep Learning","created":"2023-03-03","cited_by_count":20,"area":null},{"id":"2111.02355","title":"A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization","created":"2021-11-03","cited_by_count":19,"area":null},{"id":"2305.02251","title":"Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems","created":"2023-05-03","cited_by_count":19,"area":null},{"id":"2306.10816","title":"$\\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery","created":"2023-06-19","cited_by_count":18,"area":null},{"id":"2402.18477","title":"Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes","created":"2024-02-28","cited_by_count":18,"area":null},{"id":"2112.03555","title":"FedDAG: Federated DAG Structure Learning","created":"2021-12-07","cited_by_count":17,"area":null},{"id":"2202.03984","title":"CausPref: Causal Preference Learning for Out-of-Distribution Recommendation","created":"2022-02-08","cited_by_count":17,"area":null},{"id":"2305.17277","title":"Optimizing NOTEARS Objectives via Topological Swaps","created":"2023-05-26","cited_by_count":17,"area":null},{"id":"2406.11601","title":"Standardizing Structural Causal Models","created":"2024-06-17","cited_by_count":17,"area":null},{"id":"2110.08223","title":"Simultaneous Missing Value Imputation and Structure Learning with Groups","created":"2021-10-15","cited_by_count":16,"area":null},{"id":"2303.12703","title":"Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning","created":"2023-03-22","cited_by_count":15,"area":"lr"},{"id":"2110.06082","title":"Efficient Bayesian network structure learning via local Markov boundary search","created":"2021-10-12","cited_by_count":14,"area":null},{"id":"2301.12389","title":"On Learning Necessary and Sufficient Causal Graphs","created":"2023-01-29","cited_by_count":14,"area":null},{"id":"2303.05518","title":"Computably Continuous Reinforcement-Learning Objectives are PAC-learnable","created":"2023-03-09","cited_by_count":13,"area":null},{"id":"2306.02831","title":"MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysis","created":"2023-06-05","cited_by_count":13,"area":null},{"id":"2410.05890","title":"Ordering-Based Causal Discovery for Linear and Nonlinear Relations","created":"2024-10-08","cited_by_count":13,"area":null},{"id":"2112.10884","title":"Learning Bayesian Networks in the Presence of Structural Side Information","created":"2021-12-20","cited_by_count":12,"area":null},{"id":"2211.14897","title":"Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions","created":"2022-11-27","cited_by_count":12,"area":null},{"id":"2406.13371","title":"Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment","created":"2024-06-19","cited_by_count":12,"area":null},{"id":"2110.04719","title":"Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families","created":"2021-10-10","cited_by_count":11,"area":null},{"id":"2210.00453","title":"Neural Graphical Models","created":"2022-10-02","cited_by_count":11,"area":null},{"id":"2211.06919","title":"Towards Privacy-Aware Causal Structure Learning in Federated Setting","created":"2022-11-13","cited_by_count":11,"area":null},{"id":"2312.05540","title":"Federated Causality Learning with Explainable Adaptive Optimization","created":"2023-12-09","cited_by_count":11,"area":null},{"id":"2111.02545","title":"Multi-task Learning of Order-Consistent Causal Graphs","created":"2021-11-03","cited_by_count":10,"area":null},{"id":"2202.12813","title":"Causal discovery for observational sciences using supervised machine learning","created":"2022-02-25","cited_by_count":10,"area":null},{"id":"2208.01529","title":"CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods","created":"2022-08-02","cited_by_count":10,"area":null},{"id":"2208.06935","title":"Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables","created":"2022-08-14","cited_by_count":10,"area":null},{"id":"2209.08860","title":"A Survey of Deep Causal Models and Their Industrial Applications","created":"2022-09-19","cited_by_count":10,"area":null},{"id":"2210.14672","title":"Sparsity in Continuous-Depth Neural Networks","created":"2022-10-26","cited_by_count":10,"area":null},{"id":"2306.17378","title":"Global Optimality in Bivariate Gradient-based DAG Learning","created":"2023-06-30","cited_by_count":10,"area":null},{"id":"2309.08406","title":"Constraint-Free Structure Learning with Smooth Acyclic Orientations","created":"2023-09-15","cited_by_count":10,"area":null},{"id":"2312.12844","title":"Effective Causal Discovery under Identifiable Heteroscedastic Noise Model","created":"2023-12-20","cited_by_count":10,"area":null},{"id":"2103.15106","title":"Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding","created":"2021-03-28","cited_by_count":9,"area":null},{"id":"2301.11898","title":"DAG Learning on the Permutahedron","created":"2023-01-27","cited_by_count":9,"area":null},{"id":"2206.08119","title":"Learning to Infer Structures of Network Games","created":"2022-06-16","cited_by_count":8,"area":null},{"id":"2206.09203","title":"Interactive Visual Reasoning under Uncertainty","created":"2022-06-18","cited_by_count":8,"area":"lr"},{"id":"2301.01817","title":"Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARS","created":"2023-01-04","cited_by_count":8,"area":null},{"id":"2305.15936","title":"Learning DAGs from Data with Few Root Causes","created":"2023-05-25","cited_by_count":8,"area":null},{"id":"2609.17488","title":"LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence","created":"2026-09-15","cited_by_count":8,"area":null},{"id":"2207.07908","title":"Multiscale Causal Structure Learning","created":"2022-07-16","cited_by_count":7,"area":null},{"id":"2211.06829","title":"Methods for Recovering Conditional Independence Graphs: A Survey","created":"2022-11-13","cited_by_count":7,"area":null},{"id":"2301.12930","title":"Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing","created":"2023-01-26","cited_by_count":7,"area":null},{"id":"2305.03859","title":"Open problems in causal structure learning: A case study of COVID-19 in the UK","created":"2023-05-05","cited_by_count":7,"area":null},{"id":"2307.07973","title":"Heteroscedastic Causal Structure Learning","created":"2023-07-16","cited_by_count":7,"area":null},{"id":"2401.02930","title":"Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery","created":"2024-01-05","cited_by_count":6,"area":null},{"id":"2402.15255","title":"Optimal Transport for Structure Learning Under Missing Data","created":"2024-02-23","cited_by_count":6,"area":null},{"id":"2406.09177","title":"Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency","created":"2024-06-13","cited_by_count":6,"area":null},{"id":"2411.10154","title":"Continuous Bayesian Model Selection for Multivariate Causal Discovery","created":"2024-11-15","cited_by_count":6,"area":null},{"id":"2106.02835","title":"On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization","created":"2021-06-05","cited_by_count":5,"area":null},{"id":"2204.04644","title":"From graphs to DAGs: a low-complexity model and a scalable algorithm","created":"2022-04-10","cited_by_count":5,"area":null},{"id":"2211.17029","title":"Directed Acyclic Graph Structure Learning from Dynamic Graphs","created":"2022-11-30","cited_by_count":5,"area":null},{"id":"2303.03187","title":"Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting","created":"2023-03-06","cited_by_count":5,"area":null},{"id":"2308.07480","title":"Order-based Structure Learning with Normalizing Flows","created":"2023-08-14","cited_by_count":5,"area":null},{"id":"2402.00043","title":"Interactive and Intelligent Root Cause Analysis in Manufacturing with Causal Bayesian Networks and Knowledge Graphs","created":"2024-01-20","cited_by_count":5,"area":null},{"id":"2605.07204","title":"Arrow: A Foundation Model for Causal Discovery","created":"2026-05-08","cited_by_count":5,"area":null},{"id":"2106.01474","title":"Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning","created":"2021-06-02","cited_by_count":4,"area":null},{"id":"2110.02395","title":"Causal Explanations of Structural Causal Models","created":"2021-10-05","cited_by_count":4,"area":null},{"id":"2306.02822","title":"Discovering Dynamic Causal Space for DAG Structure Learning","created":"2023-06-05","cited_by_count":4,"area":null},{"id":"2310.18212","title":"Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice","created":"2023-10-27","cited_by_count":4,"area":null},{"id":"2405.15167","title":"ProDAG: Projected Variational Inference for Directed Acyclic Graphs","created":"2024-05-24","cited_by_count":4,"area":null},{"id":"2503.21673","title":"A friendly introduction to triangular transport","created":"2025-03-27","cited_by_count":4,"area":null},{"id":"2506.18285","title":"Learning Causal Graphs at Scale: A Foundation Model Approach","created":"2025-06-23","cited_by_count":4,"area":null},{"id":"2508.08450","title":"Differentiable Cyclic Causal Discovery Under Unmeasured Confounders","created":"2025-08-11","cited_by_count":4,"area":null},{"id":"2510.12503","title":"The Robustness of Differentiable Causal Discovery in Misspecified Scenarios","created":"2025-10-14","cited_by_count":4,"area":null},{"id":"2510.16530","title":"Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarks","created":"2025-10-18","cited_by_count":4,"area":null},{"id":"2510.22031","title":"Differentiable Constraint-Based Causal Discovery","created":"2025-10-24","cited_by_count":4,"area":null}],"next_cursor":"100"}