activity
20142023
most citedOn the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges

9 citations · 30 across the 11 of their papers we have counts for

collaborators
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8 papers · 1 filter

stat.ME2023★ 1 cited

Separable pathway effects of semi-competing risks using multi-state models

Yuhao Deng, Yi Wang, Xiang Zhan +1

Semi-competing risks refer to the phenomenon where a primary event (such as mortality) can ``censor'' an intermediate event (such as relapse of a disease), but not vice versa. Unde…

stat.ME2021

Causal Inference with Truncation-by-Death and Unmeasured Confounding

Yuhao Deng, Yingjun Chang, Xiao-Hua Zhou

Clinical studies sometimes encounter truncation by death, rendering outcomes undefined. Statistical analysis based solely on observed survivors may give biased results because the…

stat.ME2021★ 3 cited

Model-Assisted Inference for Covariate-Specific Treatment Effects with High-dimensional Data

Peng Wu, Zhiqiang Tan, Wenjie Hu +1

Covariate-specific treatment effects (CSTEs) represent heterogeneous treatment effects across subpopulations defined by certain selected covariates. In this article, we consider ma…

stat.ME2016★ 8 cited

Identification and Estimation of Causal Effects with Outcomes Truncated by Death

Linbo Wang, Xiao-Hua Zhou, Thomas S. Richardson

It is common in medical studies that the outcome of interest is truncated by death, meaning that a subject has died before the outcome could be measured. In this case, restricted a…

stat.ME2016★ 6 cited

Robust Estimation of Propensity Score Weights via Subclassification

Linbo Wang, Yuexia Zhang, Thomas S. Richardson +1

Weighting estimators based on propensity scores are widely used for causal estimation in a variety of contexts, such as observational studies, marginal structural models and interf…

stat.ME2016★ 1 cited

On estimating causal controlled direct and mediator effects for count outcomes without assuming sequential ignorability

Cheng Zheng, David C. Atkins, Melissa A. Lewis +1

Causal mediation analysis is an important statistical method in social and medical studies, as it can provide insights about why an intervention works and inform the development of…

stat.ME2015

Causal analysis of ordinal treatments and binary outcomes under truncation by death

Linbo Wang, Thomas S. Richardson, Xiao-Hua Zhou

It is common that in multiarm randomized trials, the outcome of interest is "truncated by death," meaning that it is only observed or well defined conditioning on an intermediate o…

stat.ME2014

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…