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に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
本論文は、ランダム化実験の結果を対象集団に一般化する新たな手法を提案する。従来手法では対象集団の個別データが必要だったが、本手法では共变量の要約統計量のみで一般化が可能。気候変動行動介入の実証例を通じて有効性を示した。
English
This paper proposes a novel method to generalize causal effects from randomized experiments to target populations using only summary statistics, avoiding the need for individual-level data. Illustrated with a climate change behavioral intervention study, it enhances policy relevance.
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
日本でも気候変動政策において行動介入の効果を一般化する手法は重要だが、本論文は方法論の提案であり、日本特有の文脈は薄い。ただし、SSBJ開示に関わる行動変容の評価に応用可能性がある。
In the global GX context
The method is globally relevant for generalizing behavioral intervention results, especially in climate policy. It could be applied to evaluate interventions across different populations without full data access.
👥 読者別の含意
🔬研究者:Method for generalizing RCT findings to broader populations without individual-level data.
🏢実務担当者:Could be used to assess the scalability of behavioral interventions in sustainability programs.
🏛政策担当者:Helps in designing evidence-based climate policies by generalizing trial results.
📄 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/10.1080/00273171.2026.2683348first seen 2026-06-13 04:42:01 · last seen 2026-06-16 04:46:58
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