22 citations · 91 across the 29 of their papers we have counts for
30 papers
Synthetic Nearest Neighbors: Extending Synthetic Controls for Matrix Completion with Missing Not at Random Data
Anish Agarwal, Munther Dahleh, Devavrat Shah +1
We develop a causal framework for matrix completion under missing not at random (MNAR) data. Drawing on synthetic controls from the econometric panel data literature, our approach…
Robust Matrix Estimation with Side Information
Anish Agarwal, Jungjun Choi, Ming Yuan
We introduce a flexible framework for high-dimensional matrix estimation to incorporate side information for both rows and columns. Existing approaches, such as inductive matrix co…
A Causal Inference Framework for Data Rich Environments
Alberto Abadie, Anish Agarwal, Devavrat Shah
We propose a formal model for counterfactual estimation with unobserved confounding in "data-rich" settings, i.e., where there are a large number of units and a large number of mea…
Personalized Predictions from Population Level Experiments: A Study on Alzheimer's Disease
Dennis Shen, Anish Agarwal, Vishal Misra +4
The purpose of this article is to infer patient level outcomes from population level randomized control trials (RCTs). In this pursuit, we utilize the recently proposed synthetic n…
Multi-Armed Bandits with Network Interference
Abhineet Agarwal, Anish Agarwal, Lorenzo Masoero +1
Online experimentation with interference is a common challenge in modern applications such as e-commerce and adaptive clinical trials in medicine. For example, in online marketplac…
Doubly Robust Inference in Causal Latent Factor Models
Alberto Abadie, Anish Agarwal, Raaz Dwivedi +1
This article introduces a new estimator of average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes. Th…
Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration
Daniel Ngo, Keegan Harris, Anish Agarwal +2
Synthetic control methods (SCMs) are a canonical approach used to estimate treatment effects from panel data in the internet economy. We shed light on a frequently overlooked but u…
Adaptive Principal Component Regression with Applications to Panel Data
Anish Agarwal, Keegan Harris, Justin Whitehouse +1
Principal component regression (PCR) is a popular technique for fixed-design error-in-variables regression, a generalization of the linear regression setting in which the observed…
Estimating the Value of Evidence-Based Decision Making
Alberto Abadie, Anish Agarwal, Guido Imbens +4
In an era of data abundance, statistical evidence is increasingly critical for business and policy decisions. Yet, organizations lack empirical tools to assess the value of evidenc…
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
Abhineet Agarwal, Anish Agarwal, Suhas Vijaykumar
Consider a setting where there are heterogeneous units and interventions. Our goal is to learn unit-specific potential outcomes for any combination of these interventio…
Strategyproof Decision-Making in Panel Data Settings and Beyond
Keegan Harris, Anish Agarwal, Chara Podimata +1
We consider the problem of decision-making using panel data, in which a decision-maker gets noisy, repeated measurements of multiple units (or agents). We consider a setup where th…
Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference
Anish Agarwal, Sarah H. Cen, Devavrat Shah +1
We propose a generalization of the synthetic controls and synthetic interventions methodology to incorporate network interference. We consider the estimation of unit-specific poten…
Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects
Anish Agarwal, Sukjin Han, Dwaipayan Saha +2
We propose a generalization of the synthetic control methods to the setting with dynamic treatment effects, in which each unit receives multiple treatments sequentially, according…
CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation
Abdullah Alomar, Pouya Hamadanian, Arash Nasr-Esfahany +3
We present CausalSim, a causal framework for unbiased trace-driven simulation. Current trace-driven simulators assume that the interventions being simulated (e.g., a new algorithm)…
Causal Matrix Completion
Anish Agarwal, Munther Dahleh, Devavrat Shah +1
Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations. Traditionally, it is assumed that the entries of the matrix are "missi…
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy
Anish Agarwal, Rahul Singh
The US Census Bureau will deliberately corrupt data sets derived from the 2020 US Census, enhancing the privacy of respondents while potentially reducing the precision of economic…
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators
Anish Agarwal, Abdullah Alomar, Varkey Alumootil +4
We consider offline reinforcement learning (RL) with heterogeneous agents under severe data scarcity, i.e., we only observe a single historical trajectory for every agent under an…
Causal Imputation via Synthetic Interventions
Chandler Squires, Dennis Shen, Anish Agarwal +2
Consider the problem of determining the effect of a compound on a specific cell type. To answer this question, researchers traditionally need to run an experiment applying the drug…
On Model Identification and Out-of-Sample Prediction of Principal Component Regression: Applications to Synthetic Controls
Anish Agarwal, Devavrat Shah, Dennis Shen
We analyze principal component regression (PCR) in a high-dimensional error-in-variables setting with fixed design. Under suitable conditions, we show that PCR consistently identif…
On Multivariate Singular Spectrum Analysis: Tensor and Matrix Variants
Anish Agarwal, Abdullah Alomar, Devavrat Shah
We introduce and analyze two extensions of Singular Spectrum Analysis (SSA) to the multivariate setting: a new variant of the well-known matrix-based method (mSSA), and a novel ten…
Synthetic Interventions
Anish Agarwal, Devavrat Shah, Dennis Shen
The synthetic controls (SC) methodology is a prominent tool for policy evaluation in panel data applications. Researchers commonly justify the SC framework with a low-rank matrix f…
Two Burning Questions on COVID-19: Did shutting down the economy help? Can we (partially) reopen the economy without risking the second wave?
Anish Agarwal, Abdullah Alomar, Arnab Sarker +3
As we reach the apex of the COVID-19 pandemic, the most pressing question facing us is: can we even partially reopen the economy without risking a second wave? We first need to und…
Augmented Q Imitation Learning (AQIL)
Xiao Lei Zhang, Anish Agarwal
The study of unsupervised learning can be generally divided into two categories: imitation learning and reinforcement learning. In imitation learning the machine learns by mimickin…
Towards Data Auctions with Externalities
Anish Agarwal, Munther Dahleh, Thibaut Horel +1
The design of data markets has gained importance as firms increasingly use machine learning models fueled by externally acquired training data. A key consideration is the externali…