Makar, Maggie

24 publications

TMLR 2026 Teaching Invariance Using Privileged Mediation Information Dylan Zapzalka, Maggie Makar

NeurIPS 2025 Disentangling Misreporting from Genuine Adaptation in Strategic Settings: A Causal Approach Dylan Zapzalka, Trenton Chang, Lindsay Warrenburg, Sae-Hwan Park, Daniel K Shenfeld, Ravi B Parikh, Jenna Wiens, Maggie Makar

NeurIPS 2024 Hypothesis Testing the Circuit Hypothesis in LLMs Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret, Carolina Zheng, Adrià Garriga-Alonso, Andrew Jesson, Maggie Makar, David M. Blei

ICMLW 2024 Hypothesis Testing the Circuit Hypothesis in LLMs Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret, Carolina Zheng, Adrià Garriga-Alonso, Andrew Jesson, Maggie Makar, David Blei

AISTATS 2024 Learning to Rank for Optimal Treatment Allocation Under Resource Constraints Fahad Kamran, Maggie Makar, Jenna Wiens

AISTATS 2024 Offline Policy Evaluation and Optimization Under Confounding Chinmaya Kausik, Yangyi Lu, Kevin Tan, Maggie Makar, Yixin Wang, Ambuj Tewari

UAI 2024 Partial Identification of the Maximum Mean Discrepancy with Mismeasured Data Ron Nafshi, Maggie Makar

NeurIPSW 2024 Teaching Invariance Using Privileged Mediation Information Dylan Zapzalka, Maggie Makar

NeurIPS 2024 Who’s Gaming the System? a Causally-Motivated Approach for Detecting Strategic Adaptation Trenton Chang, Lindsay Warrenburg, Sae-Hwan Park, Ravi B. Parikh, Maggie Makar, Jenna Wiens

TMLR 2023 Fairness and Robustness in Anti-Causal Prediction Maggie Makar, Alexander D'Amour

ICMLW 2023 Towards Modular Machine Learning Pipelines Aditya Modi, Jivat Neet Kaur, Maggie Makar, Pavan Mallapragada, Amit Sharma, Emre Kiciman, Adith Swaminathan

MLHC 2023 Uncovering the Varied Impact of Behavioral Change Messages on Population Groups Jiaai Xu, Rada Mihalcea, Elena Frank, Srijan Sen, Maggie Makar

AISTATS 2022 Causally Motivated Shortcut Removal Using Auxiliary Labels Maggie Makar, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, Alexander D’Amour

NeurIPS 2022 Causally Motivated Multi-Shortcut Identification and Removal Jiayun Zheng, Maggie Makar

ICMLW 2022 Causally Motivated Multi-Shortcut Identification and Removal Jiayun Zheng, Maggie Makar

NeurIPSW 2022 Conditional Differential Measurement Error: Partial Identifiability and Estimation Pengrun Huang, Maggie Makar

ICMLW 2022 Fairness and Robustness in Anti-Causal Prediction Maggie Makar, Alexander D'Amour

NeurIPS 2022 Learning Concept Credible Models for Mitigating Shortcuts Jiaxuan Wang, Sarah Jabbour, Maggie Makar, Michael Sjoding, Jenna Wiens

NeurIPS 2022 Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare Shengpu Tang, Maggie Makar, Michael Sjoding, Finale Doshi-Velez, Jenna Wiens

ICMLW 2022 Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare Shengpu Tang, Maggie Makar, Michael Sjoding, Finale Doshi-Velez, Jenna Wiens

ICML 2021 Exploiting Structured Data for Learning Contagious Diseases Under Incomplete Testing Maggie Makar, Lauren West, David Hooper, Eric Horvitz, Erica Shenoy, John Guttag

ICML 2020 Estimation of Bounds on Potential Outcomes for Decision Making Maggie Makar, Fredrik Johansson, John Guttag, David Sontag

AAAI 2019 A Distillation Approach to Data Efficient Individual Treatment Effect Estimation Maggie Makar, Adith Swaminathan, Emre Kiciman

AAAI 2018 Learning the Probability of Activation in the Presence of Latent Spreaders Maggie Makar, John V. Guttag, Jenna Wiens