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Maggie Makar’s research interests lie at the intersection of machine learning and causal inference. Her work leverages causal ideas to make ML models robust to distributional shifts, and utilizes ideas from machine learning to make causal inference more statistically efficient. She focuses on developing ML and causal models to guide decision making, particularly in the field of healthcare. Her work has appeared in ICML, AAAI, JSM, JAMA, Health Affairs, and Epidemiology.