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, and J. Pearl, "ε-Identifiability of Causal Quantities,"

Technical Report (R-16), January 2023. [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]

A. Li, S. Jiang, Y. Sun, and J. Pearl, "Learning Probabilities of Causation from Finite Population Data,"

Technical Report (R-11), October 2022. [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]