Deep Learning With DAGs
Journal article, 2025

Social science theories often postulate systems of causal relationships among variables, which are commonly represented using directed acyclic graphs (DAGs). As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify empirical evaluation, researchers typically invoke such assumptions anyway, even though they are often 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 true complexity of the system. 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 methods, cGNFs model the full joint distribution of the data using a DAG specified by the analyst, without relying on stringent assumptions about functional form. This enables flexible, non-parametric estimation of any causal estimand identified from the DAG, including total 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 controlled mobility. The article concludes with a discussion of current limitations and directions for future development.

normalizing flows

social mobility

structural equation models

directed acyclic graphs

causal inference

Author

Sourabh Balgi

Linköping University

Adel Daoud

Chalmers, Computer Science and Engineering (Chalmers)

University of Gothenburg

Jose M. Pena

Linköping University

Geoffrey T. Wodtke

Univ Chicago, Dept Sociol, 1126 E 59th St

Jesse Zhou

Univ Chicago, Dept Sociol, 1126 E 59th St

Sociological Methods and Research

0049-1241 (ISSN) 15528294 (eISSN)

Vol. In Press

Subject Categories (SSIF 2025)

Computer Sciences

Sociology

DOI

10.1177/00491241251319291

Related datasets

gtwodtke/deep_learning_with_DAGs: Replication Files for "Deep Learning with DAGs" [dataset]

URI: https://doi.org/10.5281/zenodo.14578185

More information

Latest update

3/27/2025