Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity

Background Treatment variation from observational data has been used to estimate patient-specific treatment effects. Causal Forest Algorithms (CFAs) developed for this task have unknown properties when treatment effect heterogeneity from unmeasured patient factors influences treatment choice – essen...

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Vydané v:	BMC medical research methodology Ročník 24; číslo 1; s. 66 - 15
Hlavní autori:	Brooks, John M., Chapman, Cole G., Chen, Brian K., Floyd, Sarah B., Hikmet, Neset
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 13.03.2024 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Algorithms Analysis Bias Causal Forest Algorithm (CFA) Causal Inference and Observational Data Causality Computer Simulation Computer-generated environments Decision making Estimates Health Sciences Humans Leung Ping-kwan Linear probability estimators Machine learning Medical research Medicare Medicine Medicine & Public Health Medicine, Experimental Patient Selection Patients Simulation Simulation modeling Somatotropin releasing hormone Statistical Theory and Methods Statistics for Life Sciences Surgery Theory of Medicine/Bioethics Treatment effect estimation Treatment Effect Heterogeneity Treatment outcome United States Causal Forest Algorithm (CFA) Simulation modeling Treatment effect estimation Linear probability estimators Machine learning
ISSN:	1471-2288, 1471-2288
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