Y. Xie, A. Li, "Probabilities of Causation: Bounds from Incomplete and Subpopulation Data, "
Technical Report (R-29), Submitted, May 2026. [pdf]
Y. Xie, A. Li, "Probabilities of Causation: Bounds from Incomplete and Subpopulation Data, "
Technical Report (R-29), Submitted, May 2026. [pdf]
Y. Xie, A. Li, "On the Tightness of Bounds for Probabilities of Causation, "
Technical Report (R-28), Accepted to the Causality for Decision Making Workshop at Uncertainty in Artificial Intelligence (UAI), June 2026. [pdf]
S. Wang, Y. Sun, J. Pearl, and A. Li, "Learning Probabilities of Causation with Mask-Augmented Data,"
Technical Report (R-27), Submitted, Jan 2026. [pdf]
T. Cheng, R. Mao, J. Pearl, and A. Li, "General sample size analysis for probabilities of causation: a delta method approach, "
Technical Report (R-26), Submitted, Jan 2026. [pdf]
A. Li, "Unit Selection Based on Counterfactual Logic for Business Decision Making, "
Technical Report (R-25), Dec. 2025. [pdf]
C. Cheng, A. Li, "Causal-Driven Feature Evaluation for Cross-Domain Image Classification, "
Technical Report (R-24), Submitted, Jan 2026. [pdf]
Y. Xie, A. Li, "Bounding Probabilities of Causation under Partial Causal Information, "
Technical Report (R-23), Submitted, Jan 2026. [pdf]
X. Shu, S. Wang, A. Li, "Identification of Probabilities of Causation: from Recursive to Closed-Form Bounds,"
Technical Report (R-22), Submitted, Jan 2026. [pdf]
J. He, S. Wang, A. Li, "Recover Experimental Data with Selection Bias using Counterfactual Logic,"
Technical Report (R-21), May 2025. [pdf]
A. Li, "Estimating Causal Quantities via Linear and Nonlinear Programming: Current Status, Challenges, and FutureDirections,''
Technical Report (R-19), October 2024.
In Proceedings of the 4th Workshop on Logic and Practice of Programming (LPOP), 54-56, 2024. [pdf]
Y. Qi, A. Li, "Causality in the Can: Diet Coke’s Impact on Fatness,"
Technical Report (R-18), May 2024. [pdf]
C. Zhang, A. Li, S. Mueller, and R. Iliev, "Causal AI Framework for Unit Selection in Optimizing Electric Vehicle Procurement,"
Technical Report (R-17), December 2023.
Presented at AAAI 2024 Workshop on Sustainable AI, February 2024. [pdf]
A. Li, S. Mueller, X. Shu and J. Pearl, "ε-Identifiability of Causal Quantities,"
Technical Report (R-16), Accepted by AISTATS-2026. [pdf]
A. Li, and J. Pearl, "Probabilities of Causation: Role of Observational Data,"
Technical Report (R-15), October 2022.
In Proceedings of the International Conference on Artificial Intellignece and Statistics (AISTATS-2023), 10012-10027, 2023. [pdf]
T. Harinen, R. Iliev, A. Li, S. Mueller, and C. Zhang, "Combining experimental and observational studies to estimate individual treatment effects: applications to customer journey optimization,"
Technical Report (R-14), August 2022.
Presented at KDD: 1st Workshop on End-End Customer Journey Optimization. [pdf]
A. Li, and J. Pearl, "Unit Selection: Case Study and Comparison with A/B Test Heuristic,"
Technical Report (R-13), October 2022. [pdf]
A. Li, S. Jiang, Y. Sun, and J. Pearl, "Unit Selection: Learning Benefit Function from Finite Population Data,"
Technical Report (R-12), October 2022.
Presented at NeurIPS 2022 Workshop on Causality for Real-world Impact, Dec 2022. [pdf]
S. Wang, S. Jiang, Y. Sun, J. Pearl, and A. Li, "Learning Probabilities of Causation from Finite Population Data,"
Technical Report (R-11), Revised, May 2025. [pdf]
A. Li, R. Mao, and J. Pearl, "Probabilities of Causation: Adequate Size of Experimental and Observational Samples,"
Technical Report (R-10), October 2022.
Presented at NeurIPS 2022 Workshop on Neuro Causal and Symbolic AI, Dec 2022. [pdf]
A. Li and J. Pearl, "Unit Selection with Nonbinary Treatment and Effect,"
Technical Report (R-9), August 2022.
In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), Volume 38, Technical Track 18, 20465-20472, 2024. [pdf]
A. Li and J. Pearl, "Probabilities of Causation with Nonbinary Treatment and Effect,"
Technical Report (R-8), August 2022.
In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), Volume 38, Technical Track 18, 20473-20480, 2024. [pdf]
A. Li and J. Pearl, "Bounds on Causal Effects and Application to High Dimensional Data,"
Technical Report (R-7), March 2022.
In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22), Volume 36, Technical Track 5, 5773-5780, 2022. [pdf]
A. Li and J. Pearl, "Unit Selection with Causal Diagram,"
Technical Report (R-6), March 2022.
In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22), Volume 36, Technical Track 5, 5765-5772, 2022. [pdf]
A. Li, "Unit Selection Based on Counterfactual Logic,"
Technical Report (R-5), June 2021.
Ph.D. Thesis [pdf]
S. Mueller, A. Li, and J. Pearl, "Causes of effects: Learning individual responses from population data,"
Technical Report (R-4), Revised May 2022.
In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22), L. De Raedt (Ed.), 2712-2718, 2022. [pdf]
A. Li, S. Chen, J. Qin and Z. Qin, "Training machine learning models with causal logic,"
Technical Report (R-3), June 2020.
In Companion Proceedings of the Web Conference, 557–561, 2020. [pdf]
A. Li and J. Pearl, "Unit Selection Based on Counterfactual Logic,"
Technical Report (R-2), June 2019.
In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), 1793-1799, 2019. [pdf]
A. Li and P. Schrater, "Efficient learning in linearly solvable MDP models,"
In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI-13), 248-253, 2013. [pdf]