{"total":35,"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":"2307.06250","title":"Identifiability Guarantees for Causal Disentanglement from Soft Interventions","created":"2023-07-12","cited_by_count":84,"area":null},{"id":"2305.10032","title":"A Survey on Causal Discovery: Theory and Practice","created":"2023-05-17","cited_by_count":63,"area":null},{"id":"2206.01152","title":"Causal Structure Learning: a Combinatorial Perspective","created":"2022-06-02","cited_by_count":46,"area":null},{"id":"2102.05829","title":"Causal Inference for Time series Analysis: Problems, Methods and Evaluation","created":"2021-02-11","cited_by_count":42,"area":null},{"id":"2209.04744","title":"Active Learning for Optimal Intervention Design in Causal Models","created":"2022-09-10","cited_by_count":31,"area":null},{"id":"2310.17405","title":"Causal Modeling with Stationary Diffusions","created":"2023-10-26","cited_by_count":19,"area":null},{"id":"2107.01850","title":"Matching a Desired Causal State via Shift Interventions","created":"2021-07-05","cited_by_count":16,"area":null},{"id":"2503.07302","title":"When Selection Meets Intervention: Additional Complexities in Causal Discovery","created":"2025-03-10","cited_by_count":10,"area":null},{"id":"2107.07640","title":"Obtaining Causal Information by Merging Datasets with MAXENT","created":"2021-07-15","cited_by_count":9,"area":null},{"id":"2403.14843","title":"Local Causal Discovery with Linear non-Gaussian Cyclic Models","created":"2024-03-21","cited_by_count":7,"area":null},{"id":"2208.12210","title":"Learning Relational Causal Models with Cycles through Relational Acyclification","created":"2022-08-25","cited_by_count":6,"area":null},{"id":"2210.03242","title":"Disentangling Mixtures of Unknown Causal Interventions","created":"2022-10-01","cited_by_count":6,"area":null},{"id":"2302.00293","title":"A Survey of Methods, Challenges and Perspectives in Causality","created":"2023-02-01","cited_by_count":6,"area":null},{"id":"2309.03092","title":"Establishing Markov Equivalence in Cyclic Directed Graphs","created":"2023-09-01","cited_by_count":6,"area":null},{"id":"2110.07064","title":"Variance Minimization in the Wasserstein Space for Invariant Causal Prediction","created":"2021-10-13","cited_by_count":5,"area":null},{"id":"2304.11625","title":"Meaningful Causal Aggregation and Paradoxical Confounding","created":"2023-04-23","cited_by_count":5,"area":null},{"id":"2411.14003","title":"Generative Intervention Models for Causal Perturbation Modeling","created":"2024-11-21","cited_by_count":4,"area":null},{"id":"2510.06995","title":"Root Cause Analysis of Outliers in Unknown Cyclic Graphs","created":"2025-10-08","cited_by_count":4,"area":null},{"id":"2603.04780","title":"Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning","created":"2026-03-05","cited_by_count":4,"area":null},{"id":"2112.05729","title":"Learning soft interventions in complex equilibrium systems","created":"2021-12-10","cited_by_count":2,"area":null},{"id":"2209.05598","title":"Learning domain-specific causal discovery from time series","created":"2022-09-12","cited_by_count":2,"area":null},{"id":"2507.01700","title":"Relational Causal Discovery with Latent Confounders","created":"2025-07-02","cited_by_count":2,"area":null},{"id":"2605.10163","title":"Coarsening Linear Non-Gaussian Causal Models with Cycles","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2101.06614","title":"Disentangling Observed Causal Effects from Latent Confounders using Method of Moments","created":"2021-01-17","cited_by_count":1,"area":null},{"id":"2401.13009","title":"Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders","created":"2024-01-23","cited_by_count":1,"area":null},{"id":"2603.25796","title":"Beyond identifiability: Learning causal representations with few environments and finite samples","created":"2026-03-26","cited_by_count":1,"area":null},{"id":"2303.18233","title":"Hypothesis testing on invariant subspaces of non-diagonalizable matrices with applications to network statistics","created":"2023-03-31","cited_by_count":0,"area":null},{"id":"2309.10083","title":"Invariant Probabilistic Prediction","created":"2023-09-18","cited_by_count":0,"area":null},{"id":"2510.13583","title":"On the Identifiability of Causal Graphs with the Invariance Principle","created":"2025-10-15","cited_by_count":0,"area":null},{"id":"2510.25005","title":"Cyclic Counterfactuals under Shift-Scale Interventions","created":"2025-10-28","cited_by_count":0,"area":null},{"id":"2605.13589","title":"Causal Learning with the Invariance Principle","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2605.16620","title":"SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2606.18074","title":"Tensor-based second-order causal discovery","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2607.19531","title":"Equilibrium Causal Games: Separation, Identification, and the Identifiability of Cyclic Latent States","created":"2026-07-21","cited_by_count":0,"area":null}],"next_cursor":null}