{"total":30,"rows":[{"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":"2106.15563","title":"Learning latent causal graphs via mixture oracles","created":"2021-06-29","cited_by_count":55,"area":null},{"id":"2201.10548","title":"Optimal estimation of Gaussian DAG models","created":"2022-01-25","cited_by_count":17,"area":null},{"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":"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":"2312.12844","title":"Effective Causal Discovery under Identifiable Heteroscedastic Noise Model","created":"2023-12-20","cited_by_count":10,"area":null},{"id":"2301.11898","title":"DAG Learning on the Permutahedron","created":"2023-01-27","cited_by_count":9,"area":null},{"id":"2310.17816","title":"Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs","created":"2023-10-25","cited_by_count":9,"area":null},{"id":"2307.07973","title":"Heteroscedastic Causal Structure Learning","created":"2023-07-16","cited_by_count":7,"area":null},{"id":"2410.06163","title":"Markov Equivalence and Consistency in Differentiable Structure Learning","created":"2024-10-08","cited_by_count":7,"area":null},{"id":"2111.00740","title":"Learning linear non-Gaussian directed acyclic graph with diverging number of nodes","created":"2021-11-01","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":"2111.14969","title":"A Fast Non-parametric Approach for Local Causal Structure Learning","created":"2021-11-29","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":"2512.16111","title":"BUILD with Precision: Bottom-Up Inference of Linear DAGs","created":"2025-12-18","cited_by_count":4,"area":null},{"id":"2202.01748","title":"Sequentially learning the topological ordering of causal directed acyclic graphs with likelihood ratio scores","created":"2022-02-03","cited_by_count":3,"area":null},{"id":"2309.01392","title":"Differentiable Bayesian Structure Learning with Acyclicity Assurance","created":"2023-09-04","cited_by_count":3,"area":null},{"id":"2402.06380","title":"Optimal estimation of Gaussian (poly)trees","created":"2024-02-09","cited_by_count":3,"area":null},{"id":"2511.06164","title":"Learning Gaussian DAG Models without Condition Number Bounds","created":"2025-11-08","cited_by_count":3,"area":null},{"id":"2107.10450","title":"Learning Sparse Fixed-Structure Gaussian Bayesian Networks","created":"2021-07-22","cited_by_count":2,"area":null},{"id":"2203.08339","title":"NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction","created":"2022-03-16","cited_by_count":2,"area":null},{"id":"2411.08141","title":"Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set","created":"2024-11-12","cited_by_count":2,"area":null},{"id":"2311.00674","title":"Recovering Linear Causal Models with Latent Variables via Cholesky Factorization of Covariance Matrix","created":"2023-11-01","cited_by_count":1,"area":null},{"id":"2407.04980","title":"Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows","created":"2024-07-06","cited_by_count":1,"area":null},{"id":"2106.09350","title":"Identifiability of AMP chain graph models","created":"2021-06-17","cited_by_count":0,"area":null},{"id":"2209.07028","title":"Estimating large causal polytrees from small samples","created":"2022-09-15","cited_by_count":0,"area":null},{"id":"2308.05969","title":"Learning nonparametric DAGs with incremental information via high-order HSIC","created":"2023-08-11","cited_by_count":0,"area":null},{"id":"2311.06117","title":"Distributionally Robust Skeleton Learning of Discrete Bayesian Networks","created":"2023-11-10","cited_by_count":0,"area":null},{"id":"2609.39326","title":"Discrete Score Matching Enables Causal Discovery from Count Data","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.14246","title":"Learning Non-Gaussian SEMs with Conditional Risk, and Why the Direction of Recursion Matters","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}