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 “missing …

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 …

Synthetic control (SC) methods have been widely applied to estimate the causal effect of
large-scale interventions, eg, the state-wide effect of a change in policy. The idea of …

Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose
existing approved drugs for clinical interventions. While a number of data-driven and …

We present CausalSim, a causal framework for unbiased trace-driven simulation. Current
trace-driven simulators assume that the interventions being simulated (eg, a new algorithm) …

We consider a setting where there are $ N $ heterogeneous units and $ p $ interventions.
Our goal is to learn unit-specific potential outcomes for any combination of these $ p …

When there are multiple outcome series of interest, Synthetic Control analyses typically
proceed by estimating separate weights for each outcome. In this paper, we instead propose …

We consider the problem of learning the structure of a causal directed acyclic graph (DAG)
model in the presence of latent variables. We define" latent factor causal models"(LFCMs) as …

Abstract The US Census Bureau will deliberately corrupt data sets derived from the 2020 US
Census in an effort to maintain privacy, suggesting a painful trade-off between the privacy of …

In causal inference with panel data under staggered adoption, the goal is to estimate and
derive confidence intervals for potential outcomes and treatment effects. We propose a …