13 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…
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…
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…
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…
Regression adjustment in completely randomized experiments with many covariates
Harold D Chiang, Yukitoshi Matsushita, Taisuke Otsu
This paper investigates estimation and inference for average treatment effects in completely randomized experiments when researchers observe potentially many covariates. Within Ney…
Empirical Likelihood for Random Forests and Ensembles
Harold D. Chiang, Yukitoshi Matsushita, Taisuke Otsu
We develop an empirical likelihood (EL) framework for random forests and related ensemble methods, providing a likelihood-based approach to quantify their statistical uncertainty.…