Higher-Order Least Squares: Assessing Partial Goodness of Fit of Linear Causal Models.
J Am Stat Assoc
; 119(546): 1019-1031, 2024.
Article
em En
| MEDLINE
| ID: mdl-38974187
ABSTRACT
We introduce a simple diagnostic test for assessing the overall or partial goodness of fit of a linear causal model with errors being independent of the covariates. In particular, we consider situations where hidden confounding is potentially present. We develop a method and discuss its capability to distinguish between covariates that are confounded with the response by latent variables and those that are not. Thus, we provide a test and methodology for partial goodness of fit. The test is based on comparing a novel higher-order least squares principle with ordinary least squares. In spite of its simplicity, the proposed method is extremely general and is also proven to be valid for high-dimensional settings. Supplementary materials for this article are available online.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
J Am Stat Assoc
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Suíça