anishagarwal [at] gmail [dot] com

CV, Research, Google Scholar

Anish Agarwal

Postdoctoral Scientist, Amazon, Core AI group

I will be starting as faculty at Columbia University from Fall 2023.

Most recently, I was a postdoctoral research fellow at the Simons Institute at UC Berkeley.

I did my PhD at MIT EECS, where I was fortunate to be advised by Alberto Abadie, Munther Dahleh, and Devavrat Shah. During the PhD, I interned at Microsoft Research New England with Vasilis Syrgkanis. I also served as a technical consultant to TauRx Therapeutics and Uber Technologies on questions related to experiment design and causal inference.

Prior to the PhD, I was a management consultant at Boston Consulting Group. I got my undergrad and master's degrees from Caltech under the supervision of Mani Chandy and Adam Wierman.

My current research interests are in: (1) causal machine learning; (2) high-dimensional statistics; (3) economics of data. I am particularly interested in data-driven decision-making in engineering and social systems using tools from econometrics and machine learning.

Representative Papers

Visit my Research Page for a comprehensive list of publications.

Causal Inference & Machine Learning

A. Agarwal, D. Shah, D. Shen. "Synthetic Interventions". (Video, Slides)

A. Agarwal, M. Dahleh, D. Shah, D. Shen. "Causal Matrix Completion". (Video, Slides)

A. Agarwal, R. Singh. "Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy".

A. Agarwal, A. Alomar, V. Alumootil, D. Shah, D. Shen, Z. Xu, C. Yang. "PerSim: Offline Reinforcement Learning via Personalized Simulators".

High-Dimensional Statistics: Principal Component Regression (PCR)

A. Agarwal, D. Shah, D. Shen. "On Model Identification and Out-of-Sample Prediction of PCR: Applications to Synthetic Controls". (Video, Slides)

  • An earlier related paper appeared in the Journal of the American Statistical Association, 2021 & NeurIPS, 2019 (Oral Presentation)

High-Dimensional Statistics: Time Series Analysis

A. Agarwal, A. Alomar, D. Shah. "On Multivariate Singular Spectrum Analysis and its Variants".

A. Agarwal, A. Alomar, D. Shah. "tspDB: Time Series Predict Database".

Economics of Data

A. Agarwal, M. Dahleh, T. Sarkar. "A Marketplace for Data: An Algorithmic Solution". (Video, Slides)

  • ACM Economics and Computation (EC), 2019.

Selected Talks

Chamberlain Seminar in Econometrics '21

(also at Stanford Online Causal Seminar'21)

Synthetic Interventions

Simons Institute-UC Berkeley '22

Causal Matrix Completion: Application to Offline Causal RL

Sigmetrics '22

On Multivariate Singular Spectrum Analysis and its Variants

NeurIPS Demo '21

tspDB: Time Series Predict Database

ACM Economics and Computation '19

A Marketplace for Data: An Algorithmic Solution