{"total":90,"rows":[{"id":"2109.03795","title":"Desiderata for Representation Learning: A Causal Perspective","created":"2021-09-08","cited_by_count":76,"area":null},{"id":"2110.05690","title":"Partial Counterfactual Identification from Observational and Experimental Data","created":"2021-10-12","cited_by_count":64,"area":null},{"id":"2103.11972","title":"Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals","created":"2021-03-22","cited_by_count":55,"area":null},{"id":"2103.14651","title":"Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice","created":"2021-03-27","cited_by_count":45,"area":null},{"id":"2302.02228","title":"Counterfactual Identifiability of Bijective Causal Models","created":"2023-02-04","cited_by_count":43,"area":null},{"id":"2104.13730","title":"Causes of Effects: Learning individual responses from population data","created":"2021-04-28","cited_by_count":37,"area":null},{"id":"2208.09558","title":"Personalized Decision Making -- A Conceptual Introduction","created":"2022-08-19","cited_by_count":30,"area":null},{"id":"2204.12993","title":"Counterfactual harm","created":"2022-04-27","cited_by_count":29,"area":null},{"id":"2405.16507","title":"Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning","created":"2024-05-26","cited_by_count":29,"area":null},{"id":"2208.09568","title":"Probabilities of Causation with Nonbinary Treatment and Effect","created":"2022-08-19","cited_by_count":24,"area":null},{"id":"2202.01300","title":"Causal Inference Through the Structural Causal Marginal Problem","created":"2022-02-02","cited_by_count":23,"area":null},{"id":"2106.12121","title":"Bounds on Causal Effects and Application to High Dimensional Data","created":"2021-06-23","cited_by_count":22,"area":null},{"id":"2109.01904","title":"Estimating Categorical Counterfactuals via Deep Twin Networks","created":"2021-09-04","cited_by_count":19,"area":null},{"id":"2109.07556","title":"Unit Selection with Causal Diagram","created":"2021-09-15","cited_by_count":17,"area":null},{"id":"2301.12389","title":"On Learning Necessary and Sufficient Causal Graphs","created":"2023-01-29","cited_by_count":14,"area":null},{"id":"2306.01424","title":"Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model","created":"2023-06-02","cited_by_count":14,"area":null},{"id":"2212.07056","title":"On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach","created":"2022-12-14","cited_by_count":11,"area":null},{"id":"2107.08558","title":"A Topological Perspective on Causal Inference","created":"2021-07-18","cited_by_count":10,"area":null},{"id":"2205.13832","title":"Counterfactual Analysis in Dynamic Latent State Models","created":"2022-05-27","cited_by_count":10,"area":null},{"id":"2307.16577","title":"Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources","created":"2023-07-31","cited_by_count":9,"area":null},{"id":"2210.05027","title":"Probabilities of Causation: Adequate Size of Experimental and Observational Samples","created":"2022-10-10","cited_by_count":8,"area":null},{"id":"2211.13447","title":"On the Complexity of Counterfactual Reasoning","created":"2022-11-24","cited_by_count":7,"area":null},{"id":"2306.05059","title":"Reconciling Predictive and Statistical Parity: A Causal Approach","created":"2023-06-08","cited_by_count":7,"area":null},{"id":"2510.21110","title":"Confounding Robust Deep Reinforcement Learning: A Causal Approach","created":"2025-10-24","cited_by_count":7,"area":"deeprl"},{"id":"2510.08294","title":"Counterfactual Identifiability via Dynamic Optimal Transport","created":"2025-10-09","cited_by_count":6,"area":null},{"id":"2210.05030","title":"Unit Selection: Case Study and Comparison with A/B Test Heuristic","created":"2022-10-10","cited_by_count":5,"area":null},{"id":"2210.08874","title":"Probabilities of Causation: Role of Observational Data","created":"2022-10-17","cited_by_count":5,"area":null},{"id":"2301.12022","title":"Epsilon-Identifiability of Causal Quantities","created":"2023-01-27","cited_by_count":4,"area":null},{"id":"2509.03636","title":"CausalARC: Abstract Reasoning with Causal World Models","created":"2025-09-03","cited_by_count":4,"area":"wm"},{"id":"2102.11761","title":"SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference","created":"2021-02-23","cited_by_count":3,"area":null},{"id":"2211.07264","title":"Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment","created":"2022-11-14","cited_by_count":3,"area":null},{"id":"2303.05396","title":"Bounding the Probabilities of Benefit and Harm Through Sensitivity Parameters and Proxies","created":"2023-03-08","cited_by_count":3,"area":null},{"id":"2304.02023","title":"Bounding probabilities of causation through the causal marginal problem","created":"2023-04-04","cited_by_count":3,"area":null},{"id":"2306.13242","title":"Approximate Causal Effect Identification under Weak Confounding","created":"2023-06-22","cited_by_count":3,"area":null},{"id":"2402.09165","title":"Unifying Invariance and Spuriousity for Graph Out-of-Distribution via Probability of Necessity and Sufficiency","created":"2024-02-14","cited_by_count":3,"area":null},{"id":"2506.02923","title":"The Limits of Predicting Agents from Behaviour","created":"2025-06-03","cited_by_count":3,"area":null},{"id":"2605.12924","title":"IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning","created":"2026-05-13","cited_by_count":3,"area":null},{"id":"2209.04356","title":"Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health","created":"2022-09-09","cited_by_count":2,"area":null},{"id":"2407.15273","title":"Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiency","created":"2024-07-21","cited_by_count":2,"area":null},{"id":"2411.15605","title":"GIFT: A Framework Towards Global Interpretable Faithful Textual Explanations of Vision Classifiers","created":"2024-11-23","cited_by_count":2,"area":null},{"id":"2502.08858","title":"Estimating Probabilities of Causation with Machine Learning Models","created":"2025-02-13","cited_by_count":2,"area":null},{"id":"2506.02946","title":"Abstract Counterfactuals for Language Model Agents","created":"2025-06-03","cited_by_count":2,"area":"agents"},{"id":"2509.03548","title":"Multilinear and Linear Programs for Partially Identifiable Queries in Quasi-Markovian Structural Causal Models","created":"2025-09-02","cited_by_count":2,"area":null},{"id":"2512.05373","title":"Text Rationalization for Robust Causal Effect Estimation","created":"2025-12-05","cited_by_count":2,"area":null},{"id":"2601.17160","title":"Information-Theoretic Causal Bounds under Unmeasured Confounding","created":"2026-01-23","cited_by_count":2,"area":null},{"id":"2602.02819","title":"Causal Evaluation of Membership Inference Attacks","created":"2026-02-02","cited_by_count":2,"area":"safety"},{"id":"2602.02847","title":"Causal Flow Q-Learning for Robust Offline Reinforcement Learning","created":"2026-02-02","cited_by_count":2,"area":"diffusion"},{"id":"2608.20841","title":"Foundation Models for Partial Causal Identification","created":"2026-08-21","cited_by_count":2,"area":null},{"id":"2302.14412","title":"An Algorithm and Complexity Results for Causal Unit Selection","created":"2023-02-28","cited_by_count":1,"area":null},{"id":"2310.08406","title":"Tightening Bounds on Probabilities of Causation By Merging Datasets","created":"2023-10-12","cited_by_count":1,"area":null},{"id":"2401.10101","title":"Bayesian Networks for Causal Analysis in Socioecological Systems","created":"2024-01-18","cited_by_count":1,"area":null},{"id":"2505.15274","title":"Identification of Probabilities of Causation: from Recursive to Closed-Form Bounds","created":"2025-05-21","cited_by_count":1,"area":null},{"id":"2505.17133","title":"Learning Probabilities of Causation with Mask-Augmented Data","created":"2025-05-22","cited_by_count":1,"area":null},{"id":"2512.12804","title":"Causal Counterfactuals Reconsidered","created":"2025-12-14","cited_by_count":1,"area":null},{"id":"2602.03823","title":"Preference-based Conditional Treatment Effects and Policy Learning","created":"2026-02-03","cited_by_count":1,"area":null},{"id":"2602.14503","title":"Bounding Probabilities of Causation with Partial Causal Diagrams","created":"2026-02-16","cited_by_count":1,"area":null},{"id":"2602.18762","title":"Bounds and Identification of Joint Probabilities of Potential Outcomes and Observed Variables under Monotonicity Assumptions","created":"2026-02-21","cited_by_count":1,"area":null},{"id":"2603.04534","title":"Invariant Causal Routing for Governing Social Norms in Online Market Economies","created":"2026-03-04","cited_by_count":1,"area":null},{"id":"2608.12657","title":"General Probabilities of Causation with Causal Knowledge","created":"2026-08-12","cited_by_count":1,"area":null},{"id":"2308.14474","title":"Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FI","created":"2023-08-28","cited_by_count":0,"area":null},{"id":"2309.09323","title":"Answering Causal Queries at Layer 3 with DiscoSCMs-Embracing Heterogeneity","created":"2023-09-17","cited_by_count":0,"area":null},{"id":"2309.11942","title":"On the Probability of Immunity","created":"2023-09-21","cited_by_count":0,"area":null},{"id":"2309.16409","title":"Constructing Synthetic Treatment Groups without the Mean Exchangeability Assumption","created":"2023-09-28","cited_by_count":0,"area":null},{"id":"2311.09137","title":"Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury","created":"2023-11-15","cited_by_count":0,"area":null},{"id":"2402.10240","title":"A Dynamical View of the Question of Why","created":"2024-02-14","cited_by_count":0,"area":null},{"id":"2404.19567","title":"Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment","created":"2024-04-30","cited_by_count":0,"area":null},{"id":"2405.10746","title":"Causality in the Can: Diet Coke's Impact on Fatness","created":"2024-05-17","cited_by_count":0,"area":null},{"id":"2405.15120","title":"A Counterfactual Analysis of the Dishonest Casino","created":"2024-05-24","cited_by_count":0,"area":null},{"id":"2411.01625","title":"Counterfactual explainability and analysis of variance","created":"2024-11-03","cited_by_count":0,"area":null},{"id":"2502.06398","title":"Learning Counterfactual Outcomes Under Rank Preservation","created":"2025-02-10","cited_by_count":0,"area":null},{"id":"2505.19376","title":"Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causality","created":"2025-05-26","cited_by_count":0,"area":null},{"id":"2512.08805","title":"Identifying counterfactual probabilities using bivariate distributions and uplift modeling","created":"2025-12-09","cited_by_count":0,"area":null},{"id":"2601.22736","title":"Uncertainty-aware Causal Decision Making via Effect Bound Decomposition","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2602.17070","title":"General sample size analysis for probabilities of causation: a delta method approach","created":"2026-02-19","cited_by_count":0,"area":null},{"id":"2603.20184","title":"Kolmogorov-Arnold causal generative models","created":"2026-03-20","cited_by_count":0,"area":null},{"id":"2605.06993","title":"Optimal Experiments for Partial Causal Effect Identification","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.07065","title":"Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.11373","title":"Causal Algorithmic Recourse: Foundations and Methods","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2605.31254","title":"Formalizing and falsifying causal pathways of rare events","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2607.04957","title":"Identification and Bounding of Central Moments of Causal Effects Using Marginal Moments Information","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2608.24427","title":"Partial Identification under Causal Orders by Linear Programming","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2609.03973","title":"Common-Witness Certificates and Sharp Feature Bounds for Counterfactual Image Auditing","created":"2026-09-03","cited_by_count":0,"area":null},{"id":"2609.05385","title":"Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence","created":"2026-09-04","cited_by_count":0,"area":null},{"id":"2609.18049","title":"Regional Explanations via Causal Sufficiency and Necessity","created":"2026-09-16","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},{"id":"2609.32870","title":"Counterfactual Self-Evolving Agents for Evidence-Grounded Reasoning","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.37650","title":"RACE: Relation-Level Counterfactual Explanations for Heterogeneous Graph Neural Networks","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2609.38851","title":"Where MLLMs Fail and Why: Causal Task Decomposition for Capability Failure Diagnosis","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.00372","title":"When Harnesses Lose the Signal: Causal Evaluation of Recovery in LLM Agents","created":"2026-09-30","cited_by_count":0,"area":"agents"},{"id":"2610.13112","title":"Does Location Matter? A Black-Box Causal Audit Reveals Model-Dependent Audio Grounding in LALM Hallucination","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}