The synthetic control method (SCM) is a popular approach for estimating the impact of a
treatment on a single unit in panel data settings. The “synthetic control” is a weighted …

We consider the problem of constructing bounds on the average treatment effect (ATE) when
unmeasured confounders exist but have bounded influence. Specifically, we assume that …

Inverse propensity weighting (IPW) is a popular method for estimating treatment effects from
observational data. However, its correctness relies on the untestable (and frequently …

Unobserved confounding is common in many applications, making causal inference from
observational data challenging. As a remedy, causal sensitivity analysis is an important tool …

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

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

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

In the November 2016 US presidential election, many state-level public opinion polls,
particularly in the Upper Midwest, incorrectly predicted the winning candidate. One leading …

Sensitivity analysis for the unconfoundedness assumption is a crucial component of
observational studies. The marginal sensitivity model has become increasingly popular for …

In observational studies weighting techniques are often used to overcome bias due to
confounding. Modeling approaches, such as inverse propensity score weighting, are …