22 citations · 59 across the 13 of their papers we have counts for
14 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…
From Control to Treatment: Causal Forecasting Beyond the Observed Panel
Dennis Shen
Conventional panel data methods recover retrospective counterfactuals within the observed horizon. Many policy decisions, however, are prospective: how would an untreated unit evol…
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…
Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data
Dennis Shen, Peng Ding, Jasjeet Sekhon +1
A central goal in social science is to evaluate the causal effect of a policy. One dominant approach is through panel data analysis in which the behaviors of multiple units are obs…
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…
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…
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…
mRSC: Multi-dimensional Robust Synthetic Control
Muhummad Amjad, Vishal Misra, Devavrat Shah +1
When evaluating the impact of a policy on a metric of interest, it may not be possible to conduct a randomized control trial. In settings where only observational data is available…
On Robustness of Principal Component Regression
Anish Agarwal, Devavrat Shah, Dennis Shen +1
Principal component regression (PCR) is a simple, but powerful and ubiquitously utilized method. Its effectiveness is well established when the covariates exhibit low-rank structur…
Model Agnostic Time Series Analysis via Matrix Estimation
Anish Agarwal, Muhammad Jehangir Amjad, Devavrat Shah +1
We propose an algorithm to impute and forecast a time series by transforming the observed time series into a matrix, utilizing matrix estimation to recover missing values and de-no…
Robust Synthetic Control
Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen
We present a robust generalization of the synthetic control method for comparative case studies. Like the classical method, we present an algorithm to estimate the unobservable cou…