Work

Comment: Clarifying Endogeneous Data Structures and Consequent Modelling Choices Using Causal Graphs

Year: 2020

Type: article

Abstract: We read with great interest the article by Qian, Klasnja and Murphy (2020), and commend the authors for focusing on principled estimation and providing a quantitative approach to healthcare delivery through mobile devices.The quantitative analyses studied here could have wideranging applications that may serve to increase patient empowerment by taking medical monitoring and even intervention out of the clinic and into the home.Here, we wish to delve into two complementary aspects of the work: first, we attempt to give clarifications concerning the parameter(s) of interest, and second, we provide visualizations of potential scenarios that may help to clarify estimands and when biases due to endogeneity may arise. TREATMENT EFFECTS: ONE, TWOOR TOO MANY?

Source: Statistical Science

DOI: 10.1214/20-sts777

Authors: Erica E. M. Moodie, David A. Stephens

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funder: Natural Sciences and Engineering Research Council of Canada

OpenAlex ID: W3086676289