- 著者
- Koichiro Shiba Takuya Kawahara
- 出版者
- Japan Epidemiological Association
- 雑誌
- Journal of Epidemiology (ISSN:09175040)
- 巻号頁・発行日
- pp.JE20210145, (Released:2021-06-12)
- 参考文献数
- 49
- 被引用文献数
- 1 44
Methods based on propensity score (PS) have become increasingly popular as a tool for causal inference. A better understanding of the relative advantages and disadvantages of the alternative analytic approaches can contribute to the optimal choice and use of a specific PS method over other methods. In this article, we provide an accessible overview of causal inference from observational data and two major PS-based methods (matching and inverse probability weighting), focusing on the underlying assumptions and decision-making processes. We then discuss common pitfalls and tips for applying the PS methods to empirical research and compare the conventional multivariable outcome regression and the two alternative PS-based methods (i.e., matching and inverse probability weighting) and discuss their similarities and differences. Although we note subtle differences in causal identification assumptions, we highlight that the methods are distinct primarily in terms of the statistical modeling assumptions involved and the target population for which exposure effects are being estimated for.
言及状況
外部データベース (DOI)
Twitter (72 users, 72 posts, 399 favorites)
prepensity score matchingとinverse probability weightingと重回帰の比較。Tableで特徴をまとめてくれてるので読みやすかった。(前者2つは両方propensity scoreを使うのであまり違いを意識したことがなかったのでとてもありがたかった) https://t.co/HmbnJU1CKZ
I am pleased to share our new tutorial paper on propensity score methods. We compared identifiably assumptions, modeling decisions, and causal estimand of the alternative PS-based methods and multivariable outcome regression.
https://t.co/B99dL38vsR
傾向スコアについての良さげなまとめがオープンアクセスだそうな