SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling

TL;DR

This paper introduces Super Learner Equation Modeling, a novel path modeling approach that integrates machine learning ensembles to improve causal inference, especially in non-linear settings, outperforming traditional SEM.

Contribution

It presents a new method combining Super Learner ensembles with path modeling, addressing limitations of SEM in non-linear causal effect estimation.

Findings

Provides consistent and unbiased causal effect estimates.

Performs competitively with SEM in linear models.

Outperforms SEM in non-linear relationships.

Abstract

Causal inference is a crucial goal of science, enabling researchers to arrive at meaningful conclusions regarding the predictions of hypothetical interventions using observational data. Path models, Structural Equation Models (SEMs), and, more generally, Directed Acyclic Graphs (DAGs), provide a means to unambiguously specify assumptions regarding the causal structure underlying a phenomenon. Unlike DAGs, which make very few assumptions about the functional and parametric form, SEM assumes linearity. This can result in functional misspecification which prevents researchers from undertaking reliable effect size estimation. In contrast, we propose Super Learner Equation Modeling, a path modeling technique integrating machine learning Super Learner ensembles. We empirically demonstrate its ability to provide consistent and unbiased estimates of causal effects, its competitive performance for linear models when compared with SEM, and highlight its superiority over SEM when dealing with non-linear relationships. We provide open-source code, and a tutorial notebook with example usage, accentuating the easy-to-use nature of the method.