Principal component regression (PCR) is a popular technique for fixed-de...
Business/policy decisions are often based on evidence from randomized
ex...
We consider a setting with N heterogeneous units and p interventions. Ou...
We propose a framework for decision-making in the presence of strategic
...
We propose a generalization of the synthetic controls and synthetic
inte...
We propose a generalization of the synthetic control and synthetic
inter...
Evaluating the real-world performance of network protocols is challengin...
Matrix completion is the study of recovering an underlying matrix from a...
Even the most carefully curated economic data sets have variables that a...
We consider offline reinforcement learning (RL) with heterogeneous agent...
Consider the problem of determining the effect of a drug on a specific c...
We analyze the classical method of Principal Component Regression (PCR) ...
We analyze a variant of multivariate singular spectrum analysis (mSSA), ...
We develop a method to help quantify the impact different levels of mobi...
As we reach the apex of the COVID-19 pandemic, the most pressing questio...
The study of unsupervised learning can be generally divided into two
cat...
The design of data markets has gained in importance as firms increasingl...
We consider the problem of high-dimensional error-in-variable regression...
In this work, we aim to create a data marketplace; a robust real-time
ma...
We consider the task of interpolating and forecasting a time series in t...