3 papers
stat.ME2026
A Direct Variance Estimation (DiVE) for Meta-Analysis of Median Differences
Tadahisa Okuda, Masataka Taguri, Kenichi Hayashi
Meta-analyses of two-group studies that report median differences typically rely on methods that require, in addition to the median difference and sample size, summary measures of…
stat.ME2026
Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints
Tadahisa Okuda, Shohei Shimizu, Thong Pham +2
Causal discovery has achieved substantial theoretical progress, yet its deployment in large-scale longitudinal systems remains limited. A key obstacle is that operational data are…
cs.LG2025
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
Masayuki Takayama, Tadahisa Okuda, Thong Pham +4
In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is important for reasonable causal models reflecting the broad…