  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.
