I am a data scientist at Google. Prior to Google I worked as a postdoctoral scholar in the Computational Marketing Lab of Stanford Graduate School of Business. I received my Ph.D. in Economics from UC Berkeley in 2021.

My interests include:

  • Ad measurement,

  • Digital Marketing,

  • Causal Inference,

  • Data Privacy,

  • Econometric Theory.

Email: fniu@berkeley.edu

CV: Link

LinkedIn: Link


  1. Auction Throttling and Causal Inference of Online Advertising Effects

Georege Gui, Harikesh Nair, Fengshi Niu

ACM conference on Economics and Computation (EC), 2022

  1. Kernel Density Estimation for Undirected Dyadic Data

Bryan Graham, Fengshi Niu, James Powell

Journal of Econometrics, 2022

  1. Differentially Private Estimation of Heterogeneous Causal Effects

Fengshi Niu, Harsha Nori, Brian Quistoff, Rich Caruana, Donald Ngwe, Aadharsh Kannan

Conference on Causal Learning and Reasoning (CLeaR), 2022, Oral presentation

  1. Essays on Econometrics of Dyadic Data

Fengshi Niu

Ph.D. Dissertation, 2021

Working Papers

  1. Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression

Bryan Graham, Fengshi Niu, James Powell

Revision requested by Econometric Theory, 2021

  1. Optional Intermediaries and Pricing Restraints

Chang Liu, Fengshi Niu, Alexander White

Revision requested by Journal of Economics & Management Strategy, 2021