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

arXiv: https://arxiv.org/abs/2308.04365 · HTML: https://mlsanity.com/paper/2308.04365/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 68 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 12 of 12)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)
- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery](https://mlsanity.com/paper/2103.02582/) (2021)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [Treatment effect estimation with disentangled latent factors](https://mlsanity.com/paper/2001.10652/) (2020)
- [The Influence Function of Semiparametric Estimators](https://mlsanity.com/paper/1508.01378/) (2015)
- [Intact-VAE: Estimating Treatment Effects under Unobserved Confounding](https://mlsanity.com/paper/2101.06662/) (2021)
- [BERT meets LIWC: Exploring State-of-the-Art Language Models for Predicting Communication Behavior in Couples' Conflict Interactions](https://mlsanity.com/paper/2106.01536/) (2021)
- [All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples](https://mlsanity.com/paper/2211.10310/) (2022)
- [Targeting Learning: Robust Statistics for Reproducible Research](https://mlsanity.com/paper/2006.07333/) (2020)
- [A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists](https://mlsanity.com/paper/2206.05175/) (2022)

## Built on by (most cited first, 1 of 1)

- [CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers for Causally Constrained Predictions](https://mlsanity.com/paper/2410.14485/) (2024)

## Abstract (arXiv)

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.