arXiv subjects

Shanshan Luo

Publications and source records attributed to Shanshan Luo.

22 records · Page 2Linked to original sources

arXiv2024

Efficiency-improved doubly robust estimation with non-confounding predictive covariates

In observational studies, covariates with substantial missing data are often omitted, despite their strong predictive capabilities. These excluded covariates are generally believed not to simultaneously affect both treatment and outcome, indicating that they are not genuine confounders and do not impact the identification of the average treatment effect (ATE). In this paper, we introduce an alternative doubly robust (DR) estimator that fully leverages non-confounding predictive covariates to enhance efficiency, while also allowing missing values in such covariates. Beyond the double robustness property, our proposed estimator is designed to be more efficient than the standard DR estimator. Specifically, when the propensity score model is correctly specified, it achieves the smallest asymptotic variance among the class of DR estimators, and brings additional efficiency gains by further integrating predictive covariates. Simulation studies demonstrate the notable performance of the proposed estimator over current popular methods. An illustrative example is provided to assess the effectiveness of right heart catheterization (RHC) for critically ill patients.

stat.ME↗

arXiv2023

Multiply robust estimation of causal effects using linked data

Unmeasured confounding presents a common challenge in observational studies, potentially making standard causal parameters unidentifiable without additional assumptions. Given the increasing availability of diverse data sources, exploiting data linkage offers a potential solution to mitigate unmeasured confounding within a primary study of interest. However, this approach often introduces selection bias, as data linkage is feasible only for a subset of the study population. To address this concern, we explore three nonparametric identification strategies under the assumption that a unit' s inclusion in the linked cohort is determined solely by the observed confounders, while acknowledging that the ignorability assumption may depend on some partially unobserved covariates. The existence of multiple identification strategies motivates the development of estimators that effectively capture distinct components of the observed data distribution. Appropriately combining these estimators yields triply robust estimators for the average treatment effect. These estimators remain consistent if at least one of the three distinct parts of the observed data law is correct. Moreover, they are locally efficient if all the models are correctly specified. We evaluate the proposed estimators using simulation studies and real data analysis.

stat.ME↗

arXiv2022

Identification and estimation of causal effects in the presence of confounded principal strata

The principal stratification has become a popular tool to address a broad class of causal inference questions, particularly in dealing with non-compliance and truncation-by-death problems. The causal effects within principal strata which are determined by joint potential values of the intermediate variable, also known as the principal causal effects, are often of interest in these studies. Analyses of principal causal effects from observed data in the literature mostly rely on ignorability of the treatment assignment, which requires practitioners to accurately measure as many as covariates so that all possible confounding sources are captured. However, collecting all potential confounders in observational studies is often difficult and costly, the ignorability assumption may thus be questionable. In this paper, by leveraging available negative controls that have been increasingly used to deal with uncontrolled confounding, we consider identification and estimation of causal effects when the treatment and principal strata are confounded by unobserved variables. Specifically, we show that the principal causal effects can be nonparametrically identified by invoking a pair of negative controls that are both required not to directly affect the outcome. We then relax this assumption and establish identification of principal causal effects under various semiparametric or parametric models. We also propose an estimation method of principal causal effects. Extensive simulation studies show good performance of the proposed approach and a real data application from the National Longitudinal Survey of Young Men is used for illustration.

stat.ME↗

arXiv2022

Calibrated regression estimation using empirical likelihood under data fusion

Data analysis based on information from several sources is common in economic and biomedical studies. This setting is often referred to as the data fusion problem, which differs from traditional missing data problems since no complete data is observed for any subject. We consider a regression analysis when the outcome variable and some covariates are collected from two different sources. By leveraging the common variables observed in both data sets, doubly robust estimation procedures are proposed in the literature to protect against possible model misspecifications. However, they employ only a single propensity score model for the data fusion process and a single imputation model for the covariates available in one data set. It may be questionable to assume that either model is correctly specified in practice. We therefore propose an approach that calibrates multiple propensity score and imputation models to gain more protection based on empirical likelihood methods. The resulting estimator is consistent when any one of those models is correctly specified and is robust against extreme values of the fitted propensity scores. We also establish its asymptotic normality property and discuss the semiparametric estimation efficiency. Simulation studies show that the proposed estimator has substantial advantages over existing doubly robust estimators, and an assembled U.S. household expenditure data example is used for illustration.

stat.ME↗