294 citations · 333 across the 8 of their papers we have counts for
8 papers
A Multiplicative Instrumental Variable Model for Data Missing Not-at-Random
Yunshu Zhang, Chan Park, Jiewen Liu +4
Instrumental variable (IV) methods offer a valuable approach to account for outcome data missing not-at-random. A valid missing data instrument is a measured factor which (i) predi…
Inference on Nonlinear Counterfactual Functionals under a Multiplicative IV Model
Yonghoon Lee, Mengxin Yu, Jiewen Liu +4
Instrumental variable (IV) methods play a central role in causal inference, particularly in settings where treatment assignment is confounded by unobserved variables. IV methods ha…
Proximal Causal Inference for Marginal Counterfactual Survival Curves
Andrew Ying, Yifan Cui, Eric J. Tchetgen Tchetgen
Contrasting marginal counterfactual survival curves across treatment arms is an effective and popular approach for inferring the causal effect of an intervention on a right-censore…
A New Causal Approach to Account for Treatment Switching in Randomized Experiments under a Structural Cumulative Survival Model
Andrew Ying, Eric J. Tchetgen Tchetgen
Treatment switching in a randomized controlled trial is said to occur when a patient randomized to one treatment arm switches to another treatment arm during follow-up. This can oc…
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 coherent likelihood parametrization for doubly robust estimation of a causal effect with missing confounders
Katherine Evans, Isabel Fulcher, Eric J. Tchetgen Tchetgen
Missing data and confounding are two problems researchers face in observational studies for comparative effectiveness. Williamson et al. (2012) recently proposed a unified approach…