{"total":44,"rows":[{"id":"2203.02016","title":"Interventions, Where and How? Experimental Design for Causal Models at Scale","created":"2022-03-03","cited_by_count":49,"area":null},{"id":"2206.01152","title":"Causal Structure Learning: a Combinatorial Perspective","created":"2022-06-02","cited_by_count":46,"area":null},{"id":"2109.02429","title":"Learning Neural Causal Models with Active Interventions","created":"2021-09-06","cited_by_count":40,"area":null},{"id":"2106.02988","title":"Causal Bandits with Unknown Graph Structure","created":"2021-06-05","cited_by_count":37,"area":null},{"id":"2202.02896","title":"Evaluation Methods and Measures for Causal Learning Algorithms","created":"2022-02-07","cited_by_count":23,"area":null},{"id":"2202.05100","title":"Adaptively Exploiting d-Separators with Causal Bandits","created":"2022-02-10","cited_by_count":18,"area":null},{"id":"2206.15374","title":"Verification and search algorithms for causal DAGs","created":"2022-06-30","cited_by_count":18,"area":null},{"id":"2311.02695","title":"Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions","created":"2023-11-05","cited_by_count":18,"area":null},{"id":"2105.14024","title":"Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning","created":"2021-05-28","cited_by_count":17,"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":"2302.10607","title":"Differentiable Multi-Target Causal Bayesian Experimental Design","created":"2023-02-21","cited_by_count":15,"area":null},{"id":"2208.10981","title":"Causal Entropy Optimization","created":"2022-08-23","cited_by_count":13,"area":null},{"id":"2301.03180","title":"Subset verification and search algorithms for causal DAGs","created":"2023-01-09","cited_by_count":12,"area":null},{"id":"2305.19588","title":"Active causal structure learning with advice","created":"2023-05-31","cited_by_count":10,"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":"2301.11898","title":"DAG Learning on the Permutahedron","created":"2023-01-27","cited_by_count":9,"area":null},{"id":"2205.10083","title":"A Unified Experiment Design Approach for Cyclic and Acyclic Causal Models","created":"2022-05-20","cited_by_count":7,"area":null},{"id":"2210.06594","title":"Sample Constrained Treatment Effect Estimation","created":"2022-10-12","cited_by_count":7,"area":null},{"id":"2306.05781","title":"Adaptivity Complexity for Causal Graph Discovery","created":"2023-06-09","cited_by_count":7,"area":null},{"id":"2501.10124","title":"Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders","created":"2025-01-17","cited_by_count":7,"area":null},{"id":"2405.11548","title":"Adaptive Online Experimental Design for Causal Discovery","created":"2024-05-19","cited_by_count":5,"area":null},{"id":"2401.07578","title":"Confounded Budgeted Causal Bandits","created":"2024-01-15","cited_by_count":4,"area":null},{"id":"2410.20089","title":"Sample Efficient Bayesian Learning of Causal Graphs from Interventions","created":"2024-10-26","cited_by_count":4,"area":null},{"id":"2509.21423","title":"Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models","created":"2025-09-25","cited_by_count":4,"area":null},{"id":"2305.04445","title":"New metrics and search algorithms for weighted causal DAGs","created":"2023-05-08","cited_by_count":3,"area":null},{"id":"2310.20075","title":"Meek Separators and Their Applications in Targeted Causal Discovery","created":"2023-10-30","cited_by_count":3,"area":null},{"id":"2406.08666","title":"Interventional Causal Discovery in a Mixture of DAGs","created":"2024-06-12","cited_by_count":3,"area":null},{"id":"2111.05070","title":"Universal Lower Bound for Learning Causal DAGs with Atomic Interventions","created":"2021-11-09","cited_by_count":2,"area":null},{"id":"2402.08229","title":"Causal Discovery under Off-Target Interventions","created":"2024-02-13","cited_by_count":2,"area":null},{"id":"2507.07359","title":"Goal-Oriented Sequential Bayesian Experimental Design for Causal Learning","created":"2025-07-10","cited_by_count":2,"area":null},{"id":"2509.25800","title":"Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2111.00886","title":"Intervention Efficient Algorithm for Two-Stage Causal MDPs","created":"2021-11-01","cited_by_count":1,"area":null},{"id":"2405.18626","title":"Causal Contextual Bandits with Adaptive Context","created":"2024-05-28","cited_by_count":1,"area":null},{"id":"2411.03028","title":"Graph Agnostic Causal Bayesian Optimisation","created":"2024-11-05","cited_by_count":1,"area":null},{"id":"2205.13919","title":"Fast Causal Orientation Learning in Directed Acyclic Graphs","created":"2022-05-27","cited_by_count":0,"area":null},{"id":"2406.10917","title":"Bayesian Intervention Optimization for Causal Discovery","created":"2024-06-16","cited_by_count":0,"area":null},{"id":"2503.07475","title":"Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confounding","created":"2025-03-10","cited_by_count":0,"area":null},{"id":"2504.03122","title":"From Observation to Orientation: an Adaptive Integer Programming Approach to Intervention Design","created":"2025-04-04","cited_by_count":0,"area":null},{"id":"2509.01887","title":"Design of Experiment for Discovering Directed Mixed Graph","created":"2025-09-02","cited_by_count":0,"area":null},{"id":"2511.02536","title":"Theoretical Guarantees for Causal Discovery on Large Random Graphs","created":"2025-11-04","cited_by_count":0,"area":null},{"id":"2605.08999","title":"Non-Parametric Rehearsal Learning via Conditional Mean Embeddings","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2605.13430","title":"Towards a holistic understanding of Selection Bias for Causal Effect Identification","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2609.32165","title":"FOCUS: Fixed-Confidence Online Causal Learning Using Sequential Adaptive Interventions","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.32247","title":"Certifying Interventional Agreement Among Observationally Equivalent Causal Models","created":"2026-09-26","cited_by_count":0,"area":null}],"next_cursor":null}