aa5194 [at] columbia [dot] edu

anishagarwal [at] gmail [dot] com

CV,  Google Scholar

Anish Agarwal

Assistant Professor, Columbia University


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


If you are currently a PhD student or a postdoc at Columbia University and are interested in chatting, please feel free to e-mail me.

Brief Bio

Most recently, I was a postdoctoral scientist at Amazon, Core AI, before which 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.


Selected Awards

Papers 

       Causal Inference: Matrix/Tensor Completion, Latent Factor Models, Panel Data, Matching Estimators

 A. Abadie, A. Agarwal, R. Dwivedi, A. Shah. "Doubly Robust Inference in Causal Latent Factor Models".  

 A. Agarwal, A. Agarwal, S. Vijaykumar. "Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions".  

 A. Agarwal, V. Syrgkanis. "Synthetic Blip Effects: A Causal Inference Framework for Linear Dynamical Systems".  

 A. Agarwal, S. Cen, D. Shah, C. Yu. "Network Synthetic Interventions: A Causal Inference Framework for Panel Data with Network Interference"

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

 C. Squires, D. Shen, A. Agarwal, D. Shah, C. Uhler. "Causal Imputation via Synthetic Interventions"

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


Causal Inference: Reinforcement Learning, Building Simulators from Data

A. Alomar, P. Hamadanian, A. Esfahany, A. Agarwal, M. Alizadeh, D. Shah, “CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation".

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


      

Causal Inference: Mechanism Design

D. Ngo, K. Harris, A. Agarwal, V. Syrgkanis, S. Wu. "Incentive-Aware Synthetic Controls: Counterfactual Estimation via Incentivized Exploration".

K. Harris, A. Agarwal, C. Podimata, S. Wu. "Strategyproof Decision-Making in Panel Data Settings and Beyond"

High-Dimensional Statistics: Principal Component Regression, Error-in-Variables Regression, Measurement Error

A. Agarwal, K. Harris, J. Whitehouse, S. Wu. "Adaptive Principal Component Regression with Applications to Panel Data".

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

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

A. Agarwal, D. Shah, D. Shen, D. Song. "On Robustness of Principal Component Regression"

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". 

A. Agarwal, M. Amjad, D. Shah,  D. Shen. "Model Agnostic Time Series Analysis via Matrix Estimation"

Data Markets / Value of Data

A. Abadie, A. Agarwal, G. Imbens, S. Jia, J. McQueen, S. Stepaniants. "Estimating the Value of Evidence-Based Decision Making"

A. Agarwal, M. Dahleh, T. Horel, M. Rui. "Towards Data Auctions with Externalities"

A. Agarwal, M. Dahleh, D. Shah, D. Sleeper, A. Tsai, M. Wong. "Zorro: A Model Agnostic System to Price Consumer Data."

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

Selected Talks

Stanford Online Causal Inference Seminar '23

On Causal Inference with Temporal and Spatial Spillovers with Panel Data


 Stanford Online Causal Inference Seminar '22

Causal Inference with Corrupted Data  (Talk given by Rahul Singh)


Simons Institute-UC Berkeley '22

(also at Stanford Online Causal Seminar'21, Chamberlain 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