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Deep Learning With DAGs
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. (Causality)ORCID iD: 0000-0002-3329-5533
Linköping University, Department of Management and Engineering, The Institute for Analytical Sociology, IAS. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
Department of Sociology, University of Chicago, Chicago, IL, USA.
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(English)Manuscript (preprint) (Other academic) [Artistic work]
Abstract [en]

Social science theories often postulate causal relationships among a set of variables or events. Although directed acyclic graphs (DAGs) are increasingly used to represent these theories, their full potential has not yet been realized in practice. As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify the task of empirical evaluation, researchers tend to invoke such assumptions anyway, even though they are typically arbitrary and do not reflect any theoretical content or prior knowledge. Moreover, functional form assumptions can engender bias, whenever they fail to accurately capture the complexity of the causal system under investigation. In this article, we introduce causal-graphical normalizing flows (cGNFs), a novel approach to causal inference that leverages deep neural networks to empirically evaluate theories represented as DAGs. Unlike conventional approaches, cGNFs model the full joint distribution of the data according to a DAG supplied by the analyst, without relying on stringent assumptions about functional form. In this way, the method allows for flexible, semi-parametric estimation of any causal estimand that can be identified from the DAG, including total effects, conditional effects, direct and indirect effects, and path-specific effects. We illustrate the method with a reanalysis of Blau and Duncan’s (1967) model of status attainment and Zhou’s (2019) model of conditional versus controlled mobility. To facilitate adoption, we provide open-source software together with a series of online tutorials for implementing cGNFs. The article concludes with a discussion of current limitations and directions for future development.

Keywords [en]
Causal Inference, Deep Learning, Directed Acyclic Graphs, Normalizing Flows, Social Mobility, Structural Equation Models
National Category
Computer Sciences Sociology (excluding Social Work, Social Psychology and Social Anthropology) Social Sciences Interdisciplinary
Identifiers
URN: urn:nbn:se:liu:diva-200185DOI: 10.31235/osf.io/6375yOAI: oai:DiVA.org:liu-200185DiVA, id: diva2:1827416
Funder
Swedish Research CouncilAvailable from: 2024-01-13 Created: 2024-01-13 Last updated: 2024-01-25Bibliographically approved

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Balgi, SourabhDaoud, AdelPeña, Jose M.

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Balgi, SourabhDaoud, AdelPeña, Jose M.
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The Division of Statistics and Machine LearningFaculty of Science & EngineeringThe Institute for Analytical Sociology, IASFaculty of Arts and Sciences
Computer SciencesSociology (excluding Social Work, Social Psychology and Social Anthropology)Social Sciences Interdisciplinary

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