Curriculum Vitae

I am currently the Scientific Coordinator at the Research Network Data Science at the University of Vienna.

Research Interests

  • Causal Inference
  • Machine Learning
  • Data Science
  • Experimental Design


New York University
PhD in Political Methodology
Advisors: Cyrus Samii, Neal Beck, Josh Tucker

New York, NY
Granted May 2016

  • Dissertation: Essays on Causal Inference and Machine Learning with Application to Nonprofits
  • Winner of 2015 Williams Award for Best Dissertation Proposal in Political Methodology from the Society of Political Methodology

UNC Chapel Hill
B.A. in International and Area Studies with distinction

Chapel Hill, NC
Granted June 2010


University of Vienna

April 2021 - present

Scientific Coordinator

  • Supervisor: Philipp Grohs

Facebook Core Data Science

Sept 2016 - March 2021

Research Scientist

  • Part of Eytan Bakshy’s Adaptive Experimentation team
  • Developed statistical, machine learning and experimental methodology
  • Ran adaptive and contextual field experiments with a variety of product teams
  • Integrated advanced methodologies into a toolkit for scalable and automatic experimentation intended for optimization (Ax) - released at F8 2019)
  • Developed scalable methods for robust observational causal inference as the technical lead of our “CausalML” initiative

Princeton University

September 2015 - May 2016

Pre-doctoral fellow

  • Supervised by Kosuke Imai

Facebook Core Data Science

Summer 2015

Summer Intern

  • Statistical and Decision Science Team
  • Supervised by Eytan Bakshy



Internal datacamp class on designing and analyzing experiments


NYU Undergraduate

TA for Power and Politics in America (under Jonathan Nagler)

Fall 2014

TA for Games, Strategy and Politics (under Steven Brams)

Fall 2013

NYU Graduate

TA for Quantitative Methods II (under Nathaniel Beck)

Spring 2015

TA for Quantitative Methods II (under Cyrus Samii)

Spring 2014

High Performance Computing Talk for NYU Datalab

February 2014

Introduction to R for NYU Datalab

January 2013



  • David Arbour, Drew Dimmery, and Arjun Sondhi. (2021) "Permutation Weighting." ICML
    Preprint Published
  • David Arbour, Drew Dimmery, and Anup Rao. (2021) "Efficient Balanced Treatment Assignments for Experimentation." AISTATS
    Preprint Github Published
  • My Phan, David Arbour, Drew Dimmery, and Anup Rao. (2021) "Designing Transportable Experiments Under S-admissability." AISTATS
    Preprint Github Published


  • Arjun Sondhi, David Arbour, and Drew Dimmery. (2020) "Balanced off-policy evaluation in general action spaces." AISTATS
    Preprint Published


  • Drew Dimmery, Eytan Bakshy, and Jasjeet Sekhon. (2019) "Shrinkage Estimators in Online Experiments." KDD
    Preprint Published


  • Drew Dimmery and Andrew Peterson. (2016) "Shining the Light on Dark Money: Political Spending by Nonprofits." RSF: The Russell Sage Foundation Journal of the Social Sciences


  • C.T Kullenberg, S.R. Mishra, Drew Dimmery, and the NOMAD Collaboration. (2012) "A search for single photon events in neutrino interactions." Physics Letters B

Working Papers / Non-archival


  • David Arbour, Drew Dimmery, Tung Mai, and Anup Rao. (2022) "Online Balanced Experimental Design."
  • Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel Jiang, and Eytan Bakshy. (2022) "Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies."


  • Molly Offer-Westort and Drew Dimmery. (2021) "Experimentation for Homogenous Policy Change."
  • Yan Leng and Drew Dimmery. (2021) "Calibration of Heterogeneous Treatment Effects in Random Experiments."


  • Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, and Eytan Bakshy. (2019) "Real-world Video Adaptation with Reinforcement Learning." ICML Workshop - RL4RealLife
  • Sam Daulton, Shaun Singh, Vashist Avadhanula, Drew Dimmery, and Eytan Bakshy. (2019) "Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints." NeurIPS Workshop - Safety and Robustness in Decision Making