Mechanism Learning: reverse causal inference in the presence of multiple unknown confounding through causally weighted Gaussian mixture models
This addresses the issue of causal inference in high-stakes ML applications, offering a novel method to reduce bias from confounding, though it is incremental as it builds on existing causal and mixture model techniques.
The paper tackles the problem of machine learning models learning spurious associations instead of causal relationships in observational data, proposing mechanism learning with causally weighted Gaussian Mixture Models to enable reverse causal inference despite unknown confounding, and demonstrates it discovers unbiased causal predictors where naive supervised learning fails.
A major limitation of machine learning (ML) prediction models is that they recover associational, rather than causal, predictive relationships between variables. In high-stakes automation applications of ML this is problematic, as the model often learns spurious, non-causal associations. This paper proposes mechanism learning, a simple method which uses causally weighted Gaussian Mixture Models (CW-GMMs) to deconfound observational data such that any appropriate ML model is forced to learn predictive relationships between effects and their causes (reverse causal inference), despite the potential presence of multiple unknown and unmeasured confounding. Effect variables can be very high-dimensional, and the predictive relationship nonlinear, as is common in ML applications. This novel method is widely applicable, the only requirement is the existence of a set of mechanism variables mediating the cause (prediction target) and effect (feature data), which is independent of the (unmeasured) confounding variables. We test our method on fully synthetic, semi-synthetic and real-world datasets, demonstrating that it can discover reliable, unbiased, causal ML predictors where by contrast, the same ML predictor trained naively using classical supervised learning on the original observational data, is heavily biased by spurious associations. We provide code to implement the results in the paper, online.