Causal inference involves determining whether a treatment (eg, an education program)
causes a change in outcomes (eg, academic achievement). It is well-known that causal …

Randomized controlled trials (RCT's) allow researchers to estimate causal effects in an
experimental sample with minimal identifying assumptions. However, to generalize or …

We analyze the synthetic control (SC) method in panel data settings with many units. We
assume the treatment assignment is based on unobserved heterogeneity and pretreatment …

Estimating treatment effects from observational data suffers from the issue of confounding
bias, which is induced by the imbalanced confounder distributions between the treated and …

Survey weighting allows researchers to account for bias in survey samples, due to unit
nonresponse or convenience sampling, using measured demographic covariates …

Assessing sensitivity to unmeasured confounding is an important step in observational
studies, which typically estimate effects under the assumption that all confounders are …

We provide a novel characterization of augmented balancing weights, also known as
automatic debiased machine learning (AutoDML). These popular doubly robust or de …

Weighting methods in causal inference have been widely used to achieve a desirable level
of covariate balancing. However, the existing weighting methods have desirable theoretical …

Weighting methods are popular tools for estimating causal effects, and assessing their
robustness under unobserved confounding is important in practice. Current approaches to …

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 …