5 papers · 1 filter
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…
Functional Synthetic Control Methods for Metric Space-Valued Outcomes
Ryo Okano, Daisuke Kurisu
The synthetic control method (SCM) is a widely used tool for evaluating causal effects of policy changes in panel data settings. Recent studies have extended its framework to accom…
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…
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…