Tony Liu

Assistant Professor of Computer Science @ Mount Holyoke College. PhD @ UPenn, former Scientist/PM @ Roblox.

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Hello! My research sits at the intersection of causal inference and machine learning, with a focus on observational methods: how do we build an understanding of the world when we cannot run a randomized experiment?

I develop machine learning approaches that improve observational causal methodology in order to answer causal questions in complex data domains. Alongside my awesome collaborators, I use these methods across data science applications in public health, social communication, mental wellness, and medicine.


selected publications

  1. Measuring Causal Effects of Civil Communication Without Randomization
    Tony Liu, Lyle Ungar, Konrad Kording, and Morgan McGuire
    International AAAI Conference on Web and Social Media (ICWSM), 2024
  2. Automated Detection of Causal Inference Opportunities: Regression Discontinuity Subgroup Discovery
    Tony Liu, Patrick Lawlor, Lyle Ungar, Konrad Kording, and Rahul Ladhania
    Transactions of Machine Learning Research (TMLR), 2023
  3. Data-driven exclusion criteria for instrumental variable studies
    Tony Liu, Patrick Lawlor, Lyle Ungar, and Konrad Kording
    Conference on causal learning and reasoning (CLeaR), 2022
  4. The relationship between text message sentiment and self-reported depression
    Tony Liu, Jonah Meyerhoff, Johannes C Eichstaedt, Chris J Karr, Susan M Kaiser, Konrad P Kording, David C Mohr, and Lyle H Ungar
    Journal of affective disorders, 2022
  5. Quantifying causality in data science with quasi-experiments
    Tony Liu, Lyle Ungar, and Konrad Kording
    Nature computational science, 2021
  6. Machine learning for phone-based relationship estimation: the need to consider population heterogeneity
    Tony Liu, Jennifer Nicholas, Max M Theilig, Sharath C Guntuku, Konrad Kording, David C Mohr, and Lyle Ungar
    Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2019