78 citations · 179 across the 16 of their papers we have counts for
16 papers
Proximal Path-Specific Inference
Yang Bai, Sihan Wu, Baoluo Sun +1
Causal mediation analysis has been extended to estimate path-specific effects with multiple intermediate variables, isolating treatment effects through a mediator of interest while…
Quasi Instrumental Variable Methods for Stable Hidden Confounding and Binary Outcome
Zhonghua Liu, Baoluo Sun, Ting Ye +2
Instrumental variable (IV) methods are central to causal inference from observational data, particularly when a randomized experiment is not feasible. However, of the three convent…
Mediation Analysis with Multiple Mediators under Unmeasured Mediator-Outcome Confounding
Deshanee S. Wickramarachchi, Laura Huey Mien Lim, Baoluo Sun
It is often of interest in the health and social sciences to investigate the joint mediation effects of multiple post-exposure mediating variables. Identification of such joint med…
A stableness of resistance model for nonresponse adjustment with callback data
Wang Miao, Xinyu Li, Ping Zhang +1
Nonresponse arises frequently in surveys and follow-ups are routinely made to increase the response rate. In order to monitor the follow-up process, callback data have been used in…
Semiparametric Efficient G-estimation with Invalid Instrumental Variables
Baoluo Sun, Zhonghua Liu, Eric Tchetgen Tchetgen
The instrumental variable method is widely used in the health and social sciences for identification and estimation of causal effects in the presence of potentially unmeasured conf…
GENIUS-MAWII: For Robust Mendelian Randomization with Many Weak Invalid Instruments
Ting Ye, Zhonghua Liu, Baoluo Sun +1
Mendelian randomization (MR) has become a popular approach to study causal effects by using genetic variants as instrumental variables. We propose a new MR method, GENIUS-MAWII, wh…
High-dimensional Model-assisted Inference for Local Average Treatment Effects with Instrumental Variables
Baoluo Sun, Zhiqiang Tan
Consider the problem of estimating the local average treatment effect with an instrument variable, where the instrument unconfoundedness holds after adjusting for a set of measured…
Accounting for correlated horizontal pleiotropy in two-sample Mendelian randomization using correlated instrumental variants
Qing Cheng, Baoluo Sun, Yingcun Xia +1
Mendelian randomization (MR) is a powerful approach to examine the causal relationships between health risk factors and outcomes from observational studies. Due to the proliferatio…
Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable
Baoluo Sun, Yifan Cui, Eric Tchetgen Tchetgen
Instrumental variable methods have been widely used to identify causal effects in the presence of unmeasured confounding. A key identification condition known as the exclusion rest…
On Semiparametric Instrumental Variable Estimation of Average Treatment Effects through Data Fusion
BaoLuo Sun, Wang Miao
Suppose one is interested in estimating causal effects in the presence of potentially unmeasured confounding with the aid of a valid instrumental variable. This paper investigates…
Doubly Robust Regression Analysis for Data Fusion
Katherine Evans, BaoLuo Sun, James Robins +1
This paper investigates the problem of making inference about a parametric model for the regression of an outcome variable on covariates when data are fused from two se…
The GENIUS Approach to Robust Mendelian Randomization Inference
Eric J. Tchetgen Tchetgen, BaoLuo Sun, Stefan Walter
Mendelian randomization (MR) is a popular instrumental variable (IV) approach, in which one or several genetic markers serve as IVs that can sometimes be leveraged to recover valid…
Semiparametric Estimation with Data Missing Not at Random Using an Instrumental Variable
BaoLuo Sun, Lan Liu, Wang Miao +3
Missing data occur frequently in empirical studies in health and social sciences, often compromising our ability to make accurate inferences. An outcome is said to be missing not a…
Discrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference
Eric J. Tchetgen Tchetgen, Linbo Wang, BaoLuo Sun
Nonmonotone missing data arise routinely in empirical studies of social and health sciences, and when ignored, can induce selection bias and loss of efficiency. In practice, it is…
Identification and Inference for Marginal Average Treatment Effect on the Treated With an Instrumental Variable
Lan Liu, Wang Miao, Baoluo Sun +2
In observational studies, treatments are typically not randomized and therefore estimated treatment effects may be subject to confounding bias. The instrumental variable (IV) desig…
On Inverse Probability Weighting for Nonmonotone Missing at Random Data
BaoLuo Sun, Eric J. Tchetgen Tchetgen
The development of coherent missing data models to account for nonmonotone missing at random (MAR) data by inverse probability weighting (IPW) remains to date largely unresolved. A…