Maggie Makar

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PhD candidate at CSAIL, MIT
E-mail: mmakar [at] mit [dot] edu
Twitter: @Maggiemakar
CV: As of October 2020
Google scholar profile

About me

I am a member of the Clinical and Applied ML group at CSAIL, led by John Guttag. I study machine learning, causal inference, and the intersection between the two. My work focuses on building data-efficient causal inference methods in resource-constrained settings, and building robust predictive ML models using ideas from causality.

Mentorship

Selected Publications

Full list of publications here

  • Estimation of Bounds on Potential Outcomes for Decision Making
    M. Makar, F. Johansson, J. Guttag, D. Sontag
    ICML, 2020 [paper]

  • A Distillation Approach to Data Efficient Individual Treatment Effect Estimation
    M. Makar, A. Swaminathan, E. Kiciman
    AAAI, 2019 [paper]

  • Learning the Probability of Activation in the Presence of Latent spreaders
    M. Makar, J. Guttag, J. Wiens
    AAAI, 2018 [paper]

  • Estimating the Causal Effectof Fine Particulate Matter Levels on Death and Hospitalization: Are Levels Below the Safety Standards Harmful?
    M. Makar *, J. Antonelli*, Q. Di, J. Schwartz, D. Cutler, F. Dominici
    Epidemiology, 2017 [paper]

Recent News

Sept 21, 2020 I will be presenting recent work at Microsoft's AI breakthroughs today
July 16, 2020 I will be presenting our work on causal bounds at ICML, 9am and 8pm EST
June 22, 2020 I will be giving an invited talk on our work on predicting infections at the Trustworthy and Robust AI workshop
June 2020 I will be spending this summer at Google Brain, working with Alex D'Amour
June 2020 Our paper on causal bound estimation has been accepted at ICML 2020