Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population
対象集団の個別データなしで因果効果を一般化する手法 (AI 翻訳)
Wen Wei Loh, Dongning Ren
🤖 gxceed AI 要約
日本語
本研究は、ランダム化実験の結果を対象集団に一般化する新しい統計手法を開発した。従来手法は対象集団の個別データを必要としたが、提案手法は要約統計量のみで一般化を可能にする。気候変動行動介入の事例を用いて実証し、政策関連性の向上に貢献する。
English
This paper develops a novel method to generalize causal effects from randomized experiments to target populations using only summary statistics, eliminating the need for individual-level data. It illustrates the approach with a climate change behavioral intervention study, enhancing the policy relevance of experimental findings.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の気候政策では、ランダム化比較試験の結果を全国規模に一般化する需要が高まっている。本手法は、個別データの制約が多い日本において、既存調査の要約統計量を活用したエビデンス構築を可能にする。
In the global GX context
Globally, generalizing experimental findings to policy-relevant populations is a key challenge. This method offers a practical solution when individual-level target population data are unavailable, applicable to climate interventions and beyond.
👥 読者別の含意
🔬研究者:Provides a statistical tool for generalizing experimental results when target population microdata are inaccessible.
🏛政策担当者:Enhances the policy relevance of behavioral experiments by enabling broader population inferences without individual data.
📄 Abstract（原文）
Generalizability is a perennial concern in randomized studies. While randomized studies are the gold standard for establishing causality, study samples are rarely representative of broader populations due to factors such as convenience sampling, participant self-selection, and researchers’ inclusion or exclusion criteria. To strengthen the generalizability of randomized experiments, the framework of causal effect generalizability provides a solution. However, existing methods require accessing representative individual-level data from the target population, which is often unavailable due to limited resources, data access restrictions, or privacy concerns. In this paper, we develop a novel method to generalize causal effects using only summary statistics on covariates from the target population. We illustrate the estimator using a real-world study by generalizing the impact of a climate change behavioral intervention from the study sample to a broader population. By avoiding the need for individual-level data from the target population, our method offers a practical tool for generalizing causal findings from randomized studies. We hope that the proposed method helps build more accurate theories and enhance the policy relevance of behavioral and psychological research.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.6084/m9.figshare.32647566first seen 2026-06-13 04:41:37 · last seen 2026-06-16 04:46:57
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