Work

A note on “Simple graphical rules to assess selection bias in general-population and selected-sample treatment effects” by M. B. Mathur and I. Shpitser

Year: 2024

Type: article

Abstract: This short note is a commentary on a 2024 article by Mathur and Shpitser in the Journal, with the aim to enlarge the class of graphs for which the conditional average treatment effect is nonparametrically identified, by allowing the outcome to be on the pathway between the treatment and the selection indicator. A first straightforward generalization is possible when (1) the outcome $Y$ is binary, and (2) the population prevalence of $Y$ is known a priori or can be made the object of a sensitivity analysis. Furthermore, identification of the effect is possible also for $Y$ having any nature, provided that a selection bias breaking node $V$ exists and the population prevalence of $V$ is known.

Source: American Journal of Epidemiology

DOI: 10.1093/aje/kwae337

Authors: Elena Stanghellini, Marco Doretti, Taiki Tezuka

Institutions: University of Perugia, University of Florence, Yokohama National University

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4402265178

FWCI:

Cites: 10

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Related to: 10