131 citations · 352 across the 94 of their papers we have counts for
14 papers · 1 filter
A spectral adjustment for spatial confounding
Yawen Guan, Garritt L. Page, Brian J Reich +2
Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. In this paper, we derive necessa…
Multiply robust estimation of causal effects under principal ignorability
Zhichao Jiang, Shu Yang, Peng Ding
Causal inference concerns not only the average effect of the treatment on the outcome but also the underlying mechanism through an intermediate variable of interest. Principal stra…
Causal inference methods for combining randomized trials and observational studies: a review
Bénédicte Colnet, Imke Mayer, Guanhua Chen +6
With increasing data availability, causal effects can be evaluated across different data sets, both randomized controlled trials (RCTs) and observational studies. RCTs isolate the…
Robust inference of conditional average treatment effects using dimension reduction
Ming-Yueh Huang, Shu Yang
It is important to make robust inference of the conditional average treatment effect from observational data, but this becomes challenging when the confounder is multivariate or hi…
A review of spatial causal inference methods for environmental and epidemiological applications
Brian J Reich, Shu Yang, Yawen Guan +3
The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Sp…
Data fusion methods for the heterogeneity of treatment effect and confounding function
Shu Yang, Siyi Liu, Donglin Zeng +1
The heterogeneity of treatment effect (HTE) lies at the heart of precision medicine. Randomized controlled trials are gold-standard for treatment effect estimation but are typicall…