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
vol.31, no.8, pp.457-463, 2021-08-05 (Released:2021-08-05)
参考文献数
49
被引用文献数
1 7

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 (ie, 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.

言及状況

外部データベース (DOI)

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@hommedefer3 Positivityとcommon supportの話はこちらをぜひ! https://t.co/MCnsADZDwN
A good read for all researchers and reviwers of prosensity score studies. J-STAGE Articles - Using Propensity Scores for Causal Inference: Pitfalls and Tips https://t.co/POkojt9pAE
早期公開されていた傾向スコアのチュートリアル論文、校正を経て綺麗な完全体になって公開された模様
The final version of our tutorial paper on propensity score methods is now available online. https://t.co/Fm8fgzJe3o https://t.co/73rlfmXuND

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