Causal clustering for 2-factor measurement models

Erich Kummerfeld, Joe Ramsey, Renjie Yang, Peter Spirtes, Richard Scheines

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Many social scientists are interested in inferring causal relations between "latent" variables that they cannot directly measure. One strategy commonly used to make such inferences is to use the values of variables that can be measured directly that are thought to be "indicators" of the latent variables of interest, together with a hypothesized causal graph relating the latent variables to their indicators. To use the data on the indicators to draw inferences about the causal relations between the latent variables (known as the structural model), it is necessary to hypothesize causal relations between the indicators and the latents that they are intended to indirectly measure, (known as the measurement model). The problem addressed in this paper is how to reliably infer the measurement model given measurements of the indicators, without knowing anything about the structural model, which is ultimately the question of interest. In this paper, we develop the Find- TwoFactorClusters (FTFC) algorithm, a search algorithm that, when compared to existing algorithms based on vanishing tetrad constraints, also works for a more complex class of measurement models, and does not assume that the model describing the causal relations between the latent variables is linear or acyclic.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Proceedings
PublisherSpringer Verlag
Pages34-49
Number of pages16
EditionPART 2
ISBN (Print)9783662448502
DOIs
StatePublished - 2014
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014 - Nancy, France
Duration: Sep 15 2014Sep 19 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume8725 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014
CountryFrance
CityNancy
Period9/15/149/19/14

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