講演内容：Statistical methods for integrating individual-level data with external summary data have attracted attention because of their potential to reduce data collection costs. Summary data are often accessible through public sources and relatively easy to obtain, making them a practical resource for enhancing the precision of statistical estimation. Effective utilization of summary data can enhance estimation precision, thereby saving both time and resources. However, incorporating external data introduces the risk of bias, which mainly arises from potential differences in background distributions between the current study and the external source. Model-based approaches, such as mass imputation and propensity score balancing, have been developed to integrate external summary data with internal individual-level data while mitigating these biases. Nonetheless, these methods remain vulnerable to bias resulting from model misspecification. In this talk, we propose a methodology utilizing generalized entropy balancing, designed to integrate external summary data even when derived from biased samples. Our method exhibits double robustness, offering enhanced protection against model misspecification. We illustrate the versatility and effectiveness of the proposed estimator through an application to the analysis of Nationwide Public-Access Defibrillation data in Japan.

講演内容：因果推論の方法として知られる差の差法(Difference-in-Differences, DID)と合成コントロール法(Synthetic Control, SC)について，アウトカムがユークリッドデータの場合を扱う従来の枠組みをより一般の場合に拡張することを考える．具体的には，DIDとSCをアウトカムが距離空間に値をとる場合に拡張する．拡張にあたり測地線を用いた因果効果の導入し，その推定方法を Geodesic DID (GDID), Geodesic SC (GSC)として提案する．GSCについては，時間があれば，(1) 回帰モデルを組み込んだ Augmented GSC, (2) GDIDとGSCを組み合わせたGeodesic Synthetic DID についても紹介する．また応用例として，アウトカムが組成データの場合と確率分布の場合のデータに提案手法を適用した結果についても紹介する．