1 citations · 2 across the 16 of their papers we have counts for
17 papers
Orthogonal Policy Learning with Ordinal Outcomes
Yue Zhang, Shanshan Luo, Yangbo He
Policy learning methods based on conditional average treatment effects can obscure subpopulation heterogeneity when applied to ordinal outcomes. We develop a policy learning framew…
Selecting among Missingness Models for Sequential Outcomes with Nonignorable Nonresponse
Yingying Wang, Yuan Liu, Shanshan Luo
Sequential outcomes in longitudinal studies and multi-wave surveys may be missing not at random at both earlier and later occasions. We study graphical models in which at least one…
Mediation Analysis with Multiple Mediators Subject to Missing Not at Random
Yanfei Jin, Shanshan Luo, Xueli Wang +1
Causal mediation analysis serves as a key tool for uncovering the mediating mechanisms linking treatments to outcomes. Existing methods for mediation analysis with multiple mediato…
Apportioning Causal Responsibility of Two Risk Factors for an Adverse Outcome via Counterfactual Attribution
Shanshan Luo, Yafang Deng, Qingyuan Zhao +1
Unlike traditional causal inference, which prospectively evaluates the effects of causes, apportioning causal responsibility requires a retrospective assessment to deduce the cause…
Assessing Interactive Causes of an Occurred Outcome Due to Two Binary Exposures
Shanshan Luo, Wei Li, Xueli Wang +2
In contrast to evaluating treatment effects, causal attribution analysis focuses on identifying the key factors responsible for an observed outcome. For two binary exposure variabl…
Covariate Balancing Value Estimation for Optimal Individualized Treatment Rules
Yue Zhang, Shanshan Luo, Zhi Geng +1
Learning an optimal individualized treatment rule depends on reliable value comparisons across the candidate class. Standard doubly robust estimators are consistent when either the…
Identification and estimation of causal peer effects using instrumental variables
Shanshan Luo, Kang Shuai, Yechi Zhang +2
In social science researches, causal inference regarding peer effects often faces significant challenges due to homophily bias and contextual confounding. For example, unmeasured h…
Causal Inference with Outcomes Truncated by Death and Missing Not at Random
Wei Li, Yuan Liu, Shanshan Luo +1
In clinical trials, principal stratification analysis is commonly employed to address the issue of truncation by death, where a subject dies before the outcome can be measured. How…
Automating the Selection of Proxy Variables of Unmeasured Confounders
Feng Xie, Zhengming Chen, Shanshan Luo +3
Recently, interest has grown in the use of proxy variables of unobserved confounding for inferring the causal effect in the presence of unmeasured confounders from observational da…
Assessing the causes of continuous effects by posterior effects of causes
Shanshan Luo, Yixuan Yu, Chunchen Liu +2
To evaluate a single cause of a binary effect, Dawid et al. (2014) defined the probability of causation, while Pearl (2015) defined the probabilities of necessity and sufficiency.…
Efficiency-improved doubly robust estimation with non-confounding predictive covariates
Shanshan Luo, Mengchen Shi, Wei Li +2
In observational studies, covariates with substantial missing data are often omitted, despite their strong predictive capabilities. These excluded covariates are generally believed…
On the Comparative Analysis of Average Treatment Effects Estimation via Data Combination
Peng Wu, Shanshan Luo, Zhi Geng
There is growing interest in exploring causal effects in target populations via data combination. However, most approaches are tailored to specific settings and lack comprehensive…
Multiply robust estimation of causal effects using linked data
Shanshan Luo, Yechi Zhang, Wei Li
Unmeasured confounding presents a common challenge in observational studies, potentially making standard causal parameters unidentifiable without additional assumptions. Given the…
Identifying Causal Effects Using Instrumental Variables from the Auxiliary Dataset
Kang Shuai, Shanshan Luo, Wei Li +1
Instrumental variable approaches have gained popularity for estimating causal effects in the presence of unmeasured confounders. However, the availability of instrumental variables…
Identifiability of causal effects with non-Gaussianity and auxiliary covariates
Kang Shuai, Shanshan Luo, Yue Zhang +2
Assessing causal effects in the presence of unmeasured confounding is challenging. Although auxiliary variables, such as instrumental variables, are commonly used to identify causa…
Identification and estimation of causal effects in the presence of confounded principal strata
Shanshan Luo, Wei Li, Wang Miao +1
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 p…
Calibrated regression estimation using empirical likelihood under data fusion
Wei Li, Shanshan Luo, Wangli Xu
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 fr…