9 citations · 30 across the 11 of their papers we have counts for
11 papers
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…
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
Yan Lyu, Sunhao Dai, Peng Wu +7
Accurate recommendation and reliable explanation are two key issues for modern recommender systems. However, most recommendation benchmarks only concern the prediction of user-item…
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges
Peng Wu, Haoxuan Li, Yuhao Deng +6
Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction a…
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…
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…
On rank estimators in increasing dimensions
Yanqin Fan, Fang Han, Wei Li +1
The family of rank estimators, including Han's maximum rank correlation (Han, 1987) as a notable example, has been widely exploited in studying regression problems. For these estim…
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…
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…
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…
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…
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…