Sarah Tan Hui Fen (Sarah) Tan
Google Scholar

I am a research scientist in Responsible AI at Facebook, working on fairness and experimentation topics. I am also interested in causal inference and interpretability. I received my PhD in Statistics from Cornell University, where I was advised by Giles Hooker and Martin Wells, with Thorsten Joachims and Rich Caruana on my committee. I am on the board of the Women in Machine Learning organization. I co-founded the Trustworthy ML Initiative.

Previously, I studied at Berkeley and Columbia, and worked in public policy in NYC, including the health department and public hospitals system. I was also a Data Science for Social Good fellow. I was fortunate to spend summers at Microsoft Research, working with Rich Caruana, Kori Inkpen, and Ece Kamar. Towards the end of my PhD studies, I was a visiting student and bioinformatics programmer at UCSF medical school.


I’m currently based in Seattle. You can reach me at ht395 AT cornell DOT edu.


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Code & Data

Publications and Preprints


  • Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes
    • L Yao, C Lo, I Nir, Tan, A Evnine, A Lerer, A Peysakhovich
    • Under review
    • Preliminary version in Conference on Digital Experimentation 2021 (Oral)
  • Using Explainable Boosting Machines (EBMs) to Detect Common Flaws in Data
    • Z Chen, Tan, H Nori, K Inkpen, Y Lou, R Caruana
    • Preliminary version in ECML-PKDD International Workshop and Tutorial on eXplainable Knowledge Discovery in Data Mining 2021 (Oral)
  • Practical Policy Optimization with Personalized Experimentation
    • M Garrard, H Wang, B Letham, S Singh, A Kazerouni, Tan, Z Wang, M Huang, Y Hu, C Zhou, N Zhou, E Bakshy
    • Preliminary version in NeurIPS 2021 Causal Inference Challenges in Sequential Decision Making Workshop
  • Considerations When Learning Additive Explanations for Black-Box Models
    • Tan, G Hooker, P Koch, A Gordo, R Caruana
    • Under review
    • Preliminary version in NeurIPS 2018 Machine Learning for Health Workshop
  • Investigating Human + Machine Complementarity: A Case Study on Recidivism
    • Tan, J Adebayo, K Inkpen, E Kamar
    • Under review
    • Preliminary version in NeurIPS 2018 Workshop on Ethical, Social and Governance Issues in AI (Spotlight)
  • Proximity Score Matching: Locally Adaptive Matching for Causal Inference
    • Tan, D Miller, J Savage
    • Preliminary version in NIPS 2015 Machine Learning in Healthcare Workshop
    • Lightning talk, Atlantic Causal Inference Conference 2015
    • 1 of 3 Best Student Paper Awards from American Statistical Association’s SSPA section

Journal and Conference Papers

Posters and Workshop Papers

For older publications and posters, click here.



  • I played piano and (bad) ukulele in an Indian fusion carnatic band. We have some videos here