22 citations · 92 across the 14 of their papers we have counts for
14 papers
A Local Method for Identifying Causal Relations under Markov Equivalence
Zhuangyan Fang, Yue Liu, Zhi Geng +2
Causality is important for designing interpretable and robust methods in artificial intelligence research. We propose a local approach to identify whether a variable is a cause of…
Causal Mediation Analysis with Multiple Treatments and Latent Confounders
Wei Li, Chunchen Liu, Zhi Geng +1
Causal mediation analysis is used to evaluate direct and indirect causal effects of a treatment on an outcome of interest through an intermediate variable or a mediator.It is diffi…
On the Individual Surrogate Paradox
Linquan Ma, Yunjian Yin, Lan Liu +1
When the primary outcome is difficult to collect, surrogate endpoint is typically used as a substitute. It is possible that for every individual, treatment has a positive effect on…
Causal Network Learning from Multiple Interventions of Unknown Manipulated Targets
Yango He, Zhi Geng
In this paper, we discuss structure learning of causal networks from multiple data sets obtained by external intervention experiments where we do not know what variables are manipu…
Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder
Wang Miao, Zhi Geng, Eric Tchetgen Tchetgen
We consider a causal effect that is confounded by an unobserved variable, but with observed proxy variables of the confounder. We show that, with at least two independent proxy var…
Novel Criteria to Exclude the Surrogate Paradox and Their Optimalities
Yunjian Yin, Lan Liu, Zhi Geng +1
When the primary outcome is hard to collect, surrogate endpoint is typically used as a substitute. However, even when the treatment has a positive average causal effect (ACE) on th…
Assessing the Treatment Effect Heterogeneity with a Latent Variable
Yunjian Yin, Lan Liu, Zhi Geng
The average treatment effect (ATE) is popularly used to assess the treatment effect. However, the ATE implicitly assumes a homogenous treatment effect even amongst individuals with…
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,…
Identification, Doubly Robust Estimation, and Semiparametric Efficiency Theory of Nonignorable Missing Data With a Shadow Variable
Wang Miao, Lan Liu, Eric Tchetgen Tchetgen +1
We consider identification and estimation with an outcome missing not at random (MNAR). We study an identification strategy based on a so-called shadow variable. A shadow variable…
Principal causal effect identification and surrogate endpoint evaluation by multiple trials
Zhichao Jiang, Peng Ding, Zhi Geng
Principal stratification is a causal framework to analyze randomized experiments with a post-treatment variable between the treatment and endpoint variables. Because the principal…
Semiparametric Inference of the Complier Average Causal Effect with Nonignorable Missing Outcomes
Hua Chen, Peng Ding, Zhi Geng +1
Noncompliance and missing data often occur in randomized trials, which complicate the inference of causal effects. When both noncompliance and missing data are present, previous pa…
Qualitative Evaluation of Associations by the Transitivity of the Association Signs
Zhichao Jiang, Peng Ding, Zhi Geng
We say that the signs of association measures among three variables {X, Y, Z} are transitive if a positive association measure between the variable X and the intermediate variable…
Identifiability of Subgroup Causal Effects in Randomized Experiments with Nonignorable Missing Covariates
Peng Ding, Zhi Geng
Although randomized experiments are widely regarded as the gold standard for estimating causal effects, missing data of the pretreatment covariates makes it challenging to estimate…
Learning local directed acyclic graphs based on multivariate time series data
Wanlu Deng, Zhi Geng, Hongzhe Li
Multivariate time series (MTS) data such as time course gene expression data in genomics are often collected to study the dynamic nature of the systems. These data provide importan…