36 citations · 167 across the 23 of their papers we have counts for
23 papers
Proximal Causal Inference without Uniqueness Assumptions
Jeffrey Zhang, Wei Li, Wang Miao +1
We consider identification and inference about a counterfactual outcome mean when there is unmeasured confounding using tools from proximal causal inference (Miao et al. [2018], Tc…
Doubly Robust Proximal Synthetic Controls
Hongxiang Qiu, Xu Shi, Wang Miao +2
To infer the treatment effect for a single treated unit using panel data, synthetic control methods construct a linear combination of control units' outcomes that mimics the treate…
Semiparametric Efficient Fusion of Individual Data and Summary Statistics
Wenjie Hu, Ruoyu Wang, Wei Li +1
Suppose we have individual data from an internal study and various summary statistics from relevant external studies. External summary statistics have the potential to improve stat…
Doubly Robust Proximal Causal Inference under Confounded Outcome-Dependent Sampling
Kendrick Qijun Li, Xu Shi, Wang Miao +1
Unmeasured confounding and selection bias are often of concern in observational studies and may invalidate a causal analysis if not appropriately accounted for. Under outcome-depen…
A self-censoring model for multivariate nonignorable nonmonotone missing data
Yilin Li, Wang Miao, Ilya Shpitser +1
We introduce a self-censoring model for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome is affected by its own value and is associ…
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…
Double Negative Control Inference in Test-Negative Design Studies of Vaccine Effectiveness
Kendrick Qijun Li, Xu Shi, Wang Miao +1
The test-negative design (TND) has become a standard approach to evaluate vaccine effectiveness against the risk of acquiring infectious diseases in real-world settings, such as In…
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…
Data Integration in Causal Inference
Xu Shi, Ziyang Pan, Wang Miao
Integrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This paper reviews development in…
Proximal Causal Inference for Complex Longitudinal Studies
Andrew Ying, Wang Miao, Xu Shi +1
A standard assumption for causal inference about the joint effects of time-varying treatment is that one has measured sufficient covariates to ensure that within covariate strata,…
Theory for identification and Inference with Synthetic Controls: A Proximal Causal Inference Framework
Xu Shi, Kendrick Li, Wang Miao +2
Synthetic control (SC) methods are commonly used to estimate the treatment effect on a single treated unit in panel data settings. An SC is a weighted average of control units buil…
A robust fusion-extraction procedure with summary statistics in the presence of biased sources
Ruoyu Wang, Qihua Wang, Wang Miao
Information from various data sources is increasingly available nowadays. However, some of the data sources may produce biased estimation due to commonly encountered biased samplin…
Sharp bounds for variance of treatment effect estimators in the finite population in the presence of covariates
Ruoyu Wang, Qihua Wang, Wang Miao +1
In a completely randomized experiment, the variances of treatment effect estimators in the finite population are usually not identifiable and hence not estimable. Although some est…
Semiparametric proximal causal inference
Yifan Cui, Hongming Pu, Xu Shi +2
Skepticism about the assumption of no unmeasured confounding, also known as exchangeability, is often warranted in making causal inferences from observational data; because exchang…
Improving efficiency of inference in clinical trials with external control data
Xinyu Li, Wang Miao, Fang Lu +1
Suppose we are interested in the effect of a treatment in a clinical trial. The efficiency of inference may be limited due to small sample size. However, external control data are…
Identifying effects of multiple treatments in the presence of unmeasured confounding
Wang Miao, Wenjie Hu, Elizabeth L. Ogburn +1
Identification of treatment effects in the presence of unmeasured confounding is a persistent problem in the social, biological, and medical sciences. The problem of unmeasured con…
An Introduction to Proximal Causal Learning
Eric J Tchetgen Tchetgen, Andrew Ying, Yifan Cui +2
A standard assumption for causal inference from observational data is that one has measured a sufficiently rich set of covariates to ensure that within covariate strata, subjects a…
A confounding bridge approach for double negative control inference on causal effects
Wang Miao, Xu Shi, Yilin Li +1
Unmeasured confounding is a key challenge for causal inference. In this paper, we establish a framework for unmeasured confounding adjustment with negative control variables. A neg…
Multiply Robust Causal Inference with Double Negative Control Adjustment for Categorical Unmeasured Confounding
Xu Shi, Wang Miao, Jennifer C. Nelson +1
Unmeasured confounding is a threat to causal inference in observational studies. In recent years, use of negative controls to mitigate unmeasured confounding has gained increasing…
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…
Identifiability of Normal and Normal Mixture Models With Nonignorable Missing Data
Wang Miao, Peng Ding, Zhi Geng
Missing data problems arise in many applied research studies. They may jeopardize statistical inference of the model of interest, if the missing mechanism is nonignorable, that is,…
On Varieties of Doubly Robust Estimators Under Missingness Not at Random With a Shadow Variable
Wang Miao, Eric Tchetgen Tchetgen
Suppose we are interested in the mean of an outcome variable missing not at random. Suppose however that one has available a fully observed shadow variable, which is associated wit…
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…