For importance sampling (IS), multiple proposals can be combined to address different aspects of a target distribution. There are various methods for IS with multiple proposals, including Hesterberg's stratified IS estimator, Owen and Zhou's regression estimator, and Tan's maximum likelihood...

Consider three different but related problems with auxiliary information: infinite population sampling or Monte Carlo with control variates, missing response with explanatory variables, and Poisson and rejective sampling with auxiliary variables. We demonstrate unified regression and likelihood...

Drawing inferences about the effects of exposures or treatments is a common challenge in many scientific fields. We propose two methods serving complementary purposes in causal inference. One can be used to estimate average causal effects, assuming ``no confounding" given measured covariates....

Causal approaches based on the potential outcome framework provide a useful tool for addressing noncompliance problems in randomized trials. We propose a new estimator of causal treatment effects in randomized clinical trials with noncompliance. We use the empirical likelihood approach to...

For missing data and causal inference problems, Rubin and van der Laan (2008) proposed estimators to achieve so-called improved local efficiency. We show that their estimators agree with existing estimators in the case of linear models, point out that one particular version of their estimators...

We give a definition of a bounded edge within the causal directed acyclic graph framework. A bounded edge generalizes the notion of a signed edge and is defined in terms of bounds on a ratio of survivor probabilities. We derive rules concerning the propagation of bounds. Bounds on causal effects...