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Two-stage least squares (TSLS) estimators and variants thereof are widely used to infer the effect of an exposure on an outcome using instrumental variables (IVs). They belong to a wider class of two-stage IV estimators, which are based on fitting a conditional mean model for the exposure, and then using the fitted exposure values along with the covariates as predictors in a linear model for the outcome. We show that standard TSLS estimators enjoy greater robustness to model misspecification than more general two-stage estimators. However, by potentially using a wrong exposure model, e.g. when the exposure is binary, they tend to be inefficient. In view of this, we study double-robust G-estimators instead. These use working models for the exposure, IV and outcome but only require correct specification of either the IV model or the outcome model to guarantee consistent estimation of the exposure effect. As the finite sample performance of the locally efficient G-estimator can be poor, we further develop G-estimation procedures with improved efficiency and robustness properties under misspecification of some or all working models. Simulation studies and a data analysis demonstrate drastic improvements, with remarkably good performance even when one or more working models are misspecified.
We present a general framework for using existing data to estimate the efficiency gain from using a covariate-adjusted estimator of a marginal treatment effect in a future randomized trial. We describe conditions under which it is possible to define
Differences between biological networks corresponding to disease conditions can help delineate the underlying disease mechanisms. Existing methods for differential network analysis do not account for dependence of networks on covariates. As a result,
This paper considers the instrumental variable quantile regression model (Chernozhukov and Hansen, 2005, 2013) with a binary endogenous treatment. It offers two identification results when the treatment status is not directly observed. The first resu
Regularization methods allow one to handle a variety of inferential problems where there are more covariates than cases. This allows one to consider a potentially enormous number of covariates for a problem. We exploit the power of these techniques,
Instrumental variable methods can identify causal effects even when the treatment and outcome are confounded. We study the problem of imperfect measurements of the binary instrumental variable, treatment or outcome. We first consider non-differential