36 citations · 139 across the 27 of their papers we have counts for
6 papers · 1 filter
Semiparametric proximal causal inference
Yifan Cui, Hongming Pu, Xu Shi +2
Skepticism about the assumption of no unmeasured confounding, also known as exchangeability, is often warranted in making causal inferences from observational data; because exchang…
On a necessary and sufficient identification condition of optimal treatment regimes with an instrumental variable
Yifan Cui, Eric Tchetgen Tchetgen
Unmeasured confounding is a threat to causal inference and individualized decision making. Similar to Cui and Tchetgen Tchetgen (2020); Qiu et al. (2020); Han (2020a), we consider…
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 fiducial approach to nonparametric deconvolution problem: discrete case
Yifan Cui, Jan Hannig
Fiducial inference, as generalized by Hannig et al. (2016), is applied to nonparametric g-modeling (Efron, 2016) in the discrete case. We propose a computationally efficient algori…
Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment
Haben Michael, Yifan Cui, Scott Lorch +1
Robins 1997 introduced marginal structural models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatment regimes in complex longitudinal s…
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
Yifan Cui, Michael R. Kosorok, Erik Sverdrup +2
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which c…