Research Interests
My primary research interest is the development of statistical causal inference methods to bridge the gap between theory and practice, particularly in medicine and other applied fields. I focus on proposing robust causal inference techniques that leverage machine learning to handle diverse and complex scenarios. My work also emphasizes the evaluation of effect heterogeneity. Additionally, I have authored applied research papers in epidemiology, preventive medicine, and public health, and I actively collaborate with researchers across a wide range of disciplines.
Selected Papers
Kabata D, Henderson NC, Varadhan R, Quantifying uncertainty of individualized treatment effects in right-censored survival data: a comparison of Bayesian additive regression trees and causal survival forest. Health Serv. Outcomes Res. Methodol., 2025, 10.1007/s10742-025-00362-9. View Paper
Kabata, D., Stuart, E. A., & Shintani, A. (2024). "Prognostic score-based model averaging approach for propensity score estimation." BMC Medical Research Methodology. View Paper
Kabata, D., Saeki, N., Kajimoto, K., Tanaka, R., Kakiuchi, N., Shintani, A., & Koretsune, Y. (2024). "Health guidance intervention mitigates the escalation of predicted comprehensive cardiovascular disease risk: Interrupted time series design." Preventive Medicine. View Paper
Kabata, D., & Shintani, M. (2023). "On propensity score misspecification in double/debiased machine learning for causal inference: ensemble and stratified approaches." Communications in Statistics - Simulation and Computation. View Paper
Kabata, D., & Shintani, M. (2021). "Variable selection in double/debiased machine learning for causal inference: an outcome-adaptive approach." Communications in Statistics - Simulation and Computation. View Paper
Contact
Email: daijiro.kabata[at]port.kobe-u.ac.jp
(Please replace [at] with @)