9 papers
Monotone Response for Random Objects
Daisuke Kurisu, Yuta Okamoto, Taisuke Otsu
Monotone treatment response (MTR), monotone treatment selection (MTS), and monotone instrumental variable (MIV) assumptions are widely used to partially identify counterfactual mea…
Lee Bounds for Random Objects
Daisuke Kurisu, Yuta Okamoto, Taisuke Otsu
In applied research, Lee (2009) bounds are widely applied to bound the average treatment effect in the presence of selection bias. This paper extends the methodology of Lee bounds…
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…
Difference-in-Differences with Interval Data
Daisuke Kurisu, Yuta Okamoto, Taisuke Otsu
Difference-in-differences (DID) is one of the most popular tools used to evaluate causal effects of policy interventions. This paper extends the DID methodology to accommodate inte…
Random sets from the perspective of metric statistics
Daisuke Kurisu, Yuta Okamoto, Taisuke Otsu
Since the seminal work by Beresteanu and Molinari(2008), the random set theory and related inference methods have been widely applied in partially identified econometric models. Me…
Regression Discontinuity Designs for Functional Data and Random Objects in Geodesic Spaces
Daisuke Kurisu, Yidong Zhou, Taisuke Otsu +1
Regression discontinuity designs (RDDs) are widely used for causal inference in observational studies with cutoff-based treatment assignment, primarily for Euclidean outcomes. We p…
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 with panel outcomes that are random objects in a geodesic metric space. Examples include distributions, compos…
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…
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…