| Causes of effects: Learning individual responses from population data S Mueller, A Li, J Pearl arXiv preprint arXiv:2104.13730, 2021 | 44 | 2021 |
| Personalized decision making–A conceptual introduction S Mueller, J Pearl Journal of Causal Inference 11 (1), 20220050, 2023 | 27 | 2023 |
| Causal inference in AI education: A primer A Forney, S Mueller Journal of Causal Inference 10 (1), 141-173, 2022 | 12 | 2022 |
| Which Patients are in Greater Need: A counterfactual analysis with reflections on COVID-19 S Mueller, J Pearl Causal analysis in theory and practice, 2020 | 6 | 2020 |
| Epsilon-identifiability of causal quantities A Li, S Mueller, J Pearl arXiv preprint arXiv:2301.12022, 2023 | 3 | 2023 |
| Estimating Individualized Causes of Effects by Leveraging Population Data SA Mueller University of California, Los Angeles, 2021 | 2 | 2021 |
| Perspective on ‘Harm’in Personalized Medicine–An Alternative Perspective S Mueller, J Pearl | 1 | 2023 |
| Monotonicity: Detection, Refutation, and Ramification S Mueller, J Pearl | 1 | 2023 |
| Fréchet inequalities–visualization, applications, and history S Mueller, J Pearl | 1 | 2019 |
| The phenomenology of action and the problem of free will SA Mueller The Florida State University, 2010 | 1 | 2010 |
| Learning Causes of Effects and Individual Responses from Population Data and Bayesian Networks S Mueller, A Li, J Pearl | | 2024 |
| Causal AI Framework for Unit Selection in Optimizing Electric Vehicle Procurement C Zhang, A Li, S Mueller, R Iliev 2nd Workshop on Sustainable AI, 2024 | | 2024 |
| Cognitive Systems Laboratory, Department of Computer Science, University of California, Los Angeles, Los Angeles, California, USA.{angli, scott, judea}@ cs. ucla. edu A Li, S Mueller, J Pearl | | 2023 |
| Causal inference in AI education: A primer S Mueller, A Forney | | 2022 |
| Combining experimental and observational studies to estimate individual treatment effects: applications to customer journey optimization ANG LI, S MUELLER, CHI ZHANG | | |
