The advent of the big data era brought new opportunities and challenges to draw treatment effect in fusion, that is, a mixed dataset collected from multiple sources (each source with an independent assignment mechanism). Due possibly omitted labels unmeasured confounders, traditional methods cannot estimate individual probability infer effectively. Therefore, we propose reconstruct label model ...

In this paper, several instrumental variable (IV) and instrumental variable-related methods for closed-loop system identification are considered and set in an extended IV framework. Extended IV methods require the appropriate choice of particular design variables, as the number and type of instrumental signals, data prefiltering and the choice of an appropriate norm of the extended IV-criterion...

In a two-stage linear regression model with Normal noise, I consider James–Stein type shrinkage in the estimation of the first-stage instrumental variable coefficients. For at least four instrumental variables and a single endogenous regressor, I show that the standard two-stage least-squares estimator is dominated with respect to bias. I construct the dominating estimator by a variant of James...

BACKGROUND Instrumental variable methods can estimate the causal effect of an exposure on an outcome using observational data. Many instrumental variable methods assume that the exposure-outcome relation is linear, but in practice this assumption is often in doubt, or perhaps the shape of the relation is a target for investigation. We investigate this issue in the context of Mendelian randomiza...