SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling
This method addresses causal inference for researchers using observational data, offering a more flexible alternative to traditional Structural Equation Models, though it is incremental as it builds on existing Super Learner and path modeling techniques.
The authors tackled the problem of functional misspecification in causal inference by proposing Super Learner Equation Modeling (SLEM), which integrates machine learning ensembles into path modeling, and demonstrated its ability to provide consistent and unbiased causal effect estimates with competitive performance in linear models and superiority in non-linear cases.
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.