1 citations · 1 across the 5 of their papers we have counts for
5 papers
A Robust Framework for Two-Sample Mendelian Randomization under Population Heterogeneity
Dingke Tang, Xuming He, Shu Yang
Mendelian randomization is a powerful tool for causal inference in observational studies. The two-sample summary-data design, which estimates genetic associations with exposures an…
Average Treatment Effect Estimation with Non-binary Instrumental Variables
Mei Dong, Lin Liu, Dingke Tang +3
Non-binary instrumental variables, especially continuous ones, are common in practice. A binary recoding induces a Wald ratio but may discard useful variation and reduce efficiency…
The synthetic instrument: From sparse association to sparse causation
Dingke Tang, Dehan Kong, Linbo Wang
In many observational studies, researchers are often interested in studying the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data suc…
The Promises of Parallel Outcomes
Ying Zhou, Dingke Tang, Dehan Kong +1
A key challenge in causal inference from observational studies is the identification and estimation of causal effects in the presence of unmeasured confounding. In this paper, we i…
Ultra-high Dimensional Variable Selection for Doubly Robust Causal Inference
Dingke Tang, Dehan Kong, Wenliang Pan +1
Causal inference has been increasingly reliant on observational studies with rich covariate information. To build tractable causal procedures, such as the doubly robust estimators,…