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Activity Number: 65 - Causal Inference with Latent Variables
Type: Invited
Date/Time: Monday, August 9, 2021 : 10:00 AM to 11:50 AM
Sponsor: Mental Health Statistics Section
Abstract #316708
Title: A Statistical Test to Reject the Structural Interpretation of a Latent Factor Model
Author(s): Tyler J VanderWeele* and Stijn Vansteelandt
Companies: Harvard University and Ghent University
Keywords: Factor analysis; Structural equation modeling; Measurement; Reflectie Model; Causal Inference

Factor analysis is often used to assess whether a single univariate latent variable is sufficient to explain most of the covariance among a set of indicators for some underlying construct. When evidence suggests that a single factor is adequate, research often proceeds by using a univariate summary of the indicators in subsequent research. Implicit in such practices is the assumption that it is the underlying latent, rather than the indicators, that is causally efficacious. The assumption that the indicators do not have effects on anything subsequent, and that they are themselves only affected by antecedents through the underlying latent is a strong assumption, effectively imposing a structural interpretation on the latent factor model. In this paper, we show that this structural assumption has empirically testable implications, even though the latent is unobserved. We develop a statistical test to potentially reject the structural interpretation of a latent factor model. We apply this test to data concerning associations between the Satisfaction-with-Life-Scale and subsequent mortality, which provides strong evidence against a structural univariate latent underlying this scale.

Authors who are presenting talks have a * after their name.

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