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Causality in the Sciences$
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Phyllis McKay Illari, Federica Russo, and Jon Williamson

Print publication date: 2011

Print ISBN-13: 9780199574131

Published to Oxford Scholarship Online: September 2011

DOI: 10.1093/acprof:oso/9780199574131.001.0001

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Measuring latent causal structure

Measuring latent causal structure

Chapter:
(p.673) 32 Measuring latent causal structure
Source:
Causality in the Sciences
Author(s):

Ricardo Silva

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199574131.003.0032

The presence of latent variables makes the task of estimating causal effects difficult. In particular, it might not even be possible to record important variables without measurement error, a common fact in fields such as psychology and social sciences. A fair amount of theory is often used to design instruments to indirectly measure such latent variables, such that one obtains estimates of measurement error. If the measurement error is known, then causal effects can be identified in a variety of scenarios. Unfortunately, a strictly theoretical approach for formalizing a measurement model is error prone and does not provide alternative models that could equally or better explain the data. The chapter introduces an algorithmic approach that, given a set of observed indicators of latent phenomena of interest and common assumptions about the causal structure of the world, provides a set of measurement models compatible with the observed data. This approach extends previous results in the literature which would select an observed variable only if it measured a single latent variable. The extensions cover cases where some variables are allowed to be indicators of more than one hidden common cause.

Keywords:   latent variable modeling, measurement error, causal discovery, structural equation models

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