Can predictive models be used for causal inference?

TL;DR

This paper demonstrates that with causal feature selection, machine learning and deep learning models can be used for more accurate causal inference and better generalization, challenging the idea that prediction and explanation are fundamentally at odds.

Contribution

It shows that constraining ML and DL models with causal feature selection allows near-unbiased effect estimation and improves generalization, bridging the gap between prediction and causal inference.

Findings

Causal feature selection reduces bias in effect estimates.

Hyperparameter tuning differs for prediction versus inference.

Causally constrained models generalize better to new data.

Abstract

Supervised machine learning (ML) and deep learning (DL) algorithms excel at predictive tasks, but it is commonly assumed that they often do so by exploiting non-causal correlations, which may limit both interpretability and generalizability. Here, we show that this trade-off between explanation and prediction is not as deep and fundamental as expected. Whereas ML and DL algorithms will indeed tend to use non-causal features for prediction when fed indiscriminately with all data, it is possible to constrain the learning process of any ML and DL algorithm by selecting features according to Pearl's backdoor adjustment criterion. In such a situation, some algorithms, in particular deep neural networks, can provide near unbiased effect estimates under feature collinearity. Remaining biases are explained by the specific algorithmic structures as well as hyperparameter choice. Consequently, optimal hyperparameter settings are different when tuned for prediction or inference, confirming the general expectation of a trade-off between prediction and explanation. However, the effect of this trade-off is small compared to the effect of a causally constrained feature selection. Thus, once the causal relationship between the features is accounted for, the difference between prediction and explanation may be much smaller than commonly assumed. We also show that such causally constrained models generalize better to new data with altered collinearity structures, suggesting generalization failure may often be due to a lack of causal learning. Our results not only provide a perspective for using ML for inference of (causal) effects but also help to improve the generalizability of fitted ML and DL models to new data.