5 papers
Geodesic Causal Inference
Daisuke Kurisu, Yidong Zhou, Taisuke Otsu +1
Adjusting for confounding and imbalance when establishing statistical relationships is an increasingly important task, and causal inference methods have emerged as the most popular…
Geodesic Synthetic Control Methods for Random Objects and Functional Data
Daisuke Kurisu, Yidong Zhou, Taisuke Otsu +1
We introduce a geodesic synthetic control method for causal inference that extends existing synthetic control methods to scenarios where outcomes are elements in a geodesic metric…
Sensitivity Analysis when Generalizing Causal Effects from Multiple Studies to a Target Population: Motivation from the ECHO Program
Bolun Liu, Trang Quynh Nguyen, Elizabeth A. Stuart +22
Unobserved effect modifiers can induce bias when generalizing causal effect estimates to target populations. In this work, we extend a sensitivity analysis framework assessing the…
Regression Discontinuity Designs for Functional Data and Random Objects in Geodesic Spaces
Daisuke Kurisu, Yidong Zhou, Taisuke Otsu +1
Regression discontinuity designs have been widely used in observational studies to estimate causal effects of an intervention or treatment at a cutoff point. We propose a generaliz…
Geodesic Difference-in-Differences
Yidong Zhou, Daisuke Kurisu, Taisuke Otsu +1
Difference-in-differences (DID) is a widely used quasi-experimental design for causal inference, traditionally applied to scalar or Euclidean outcomes, while extensions to outcomes…