Greg Lewis

Greg Lewis

I work in economics, causal machine learning and marketplace design. I have previously been a senior principal economist at Amazon (where I was part of the Pharmacy leadership team and worked on the design of RxPass and offering customers insurance price estimates, among other things), senior principal researcher at Microsoft Research (where Vasilis Syrgkanis and I co-led the ALICE group that builds and maintains EconML) and associate professor of economics at Harvard. Here is my CV. You can also find me on LinkedIn and Google Scholar.

Current Working Papers
  1. You Can Lead a Horse to Water: Spatial Learning and Path Dependence in Consumer Search (with Charles Hodgson), conditionally accepted at Econometrica
  2. Dynamic Demand Estimation in Auction Markets (with Matt Backus), accepted for publication at Review of Economic Studies
  3. Online Search and Product Rankings: A Double Index Approach (with Giovanni Compiani, Sida Peng and Will Wang), accepted at Marketing Science
  4. Learning Product Characteristics and Consumer Preferences from Search Data (with Luis Armona and Giorgos Zervas), R&R at Marketing Science
  5. Maximum Likelihood Estimation of Differentiated Products Demand Systems (with Bora Ozaltun and Giorgos Zervas)
  6. Mostly Harmless Machine Learning: Learning Optimal Instruments in Linear IV Models (with Daniel Chen and Kevin Chen)
Journal Publications
  1. Consumer Control and Privacy Policies (with Nageeb Ali and Shoshanna Vasserman), American Economic Review Papers and Proceedings 2023
  2. Voluntary Disclosure and Personalized Pricing (with Nageeb Ali and Shoshanna Vasserman), Review of Economic Studies 2023 (featured article)
  3. Just starting out: Learning and equilibrium in a new market (with Ulrich Doraszelski and Ariel Pakes), American Economic Review 2018 (lead article)
  4. Buy-it-now or Take-a-chance: Price Discrimination through Randomized Auctions (with Elisa Celis, Markus Mobius and Hamid Nazerzadeh), Management Science 2014
  5. Moral Hazard, Incentive Contracts and Risk: Evidence from Procurement (with Patrick Bajari), Review of Economic Studies 2014
  6. Student Portfolios and the College Admissions Problem (with Hector Chade and Lones Smith), Review of Economic Studies 2014
  7. Procurement with Time Incentives: Theory and Evidence (with Patrick Bajari), Quarterly Journal of Economics 2011
  8. Asymmetric Information, Adverse Selection and Online Disclosure: The Case of eBay Motors, American Economic Review 2011
Refereed Conference Publications
  1. Non-Parametric Inference Adaptive to Intrinsic Dimension (with Khashayar Khosravi and Vasilis Syrgkanis), PMLR 2022
  2. Estimating the long-term effects of novel treatments (with Keith Battochi, Eleanor Dillon, Maggie Hei, Miruna Oprescu and Vasilis Syrgkanis), NeurIPS 2021
  3. Double/Debiased Machine Learning for Dynamic Treatment Effects via g-estimation (with Vasilis Syrgkanis), NeurIPS 2021
  4. Minimax Estimation of Conditional Moment Models(with Nishanth Dikkala, Lester Mackey and Vasilis Syrgkanis), NeurIPS 2020
  5. Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments (with Keith Battochi, Maggie Hei, Victor Lei, Miruna Oprescu and Vasilis Syrgkanis), NeurIPS 2019
  6. Semi-Parametric Efficient Policy Learning with Continuous Actions (with Victor Chernozhukov, Mert Demirer and Vasilis Syrgkanis), NeurIPS 2019
  7. Counterfactual Prediction with Deep Instrumental Variables Networks (with Jason Hartford, Kevin Leyton-Brown and Matt Taddy), ICML 2017
Dormant Working Papers
  1. Adversarial Generalized Method of Moments (with Vasilis Syrgkanis), superseded by the minimax estimation paper above
  2. A Price Theoretic Model of Search Intermediation by Online Platforms (with Gleb Romanyuk and Albert Wang)