Learning Interpretable Models with Causal Guarantees
This work addresses the need for interpretable and causal models in high-stakes domains like medicine, law, and finance, though it is incremental as it builds on existing supervised learning methods.
The paper tackles the problem of learning interpretable models with causal guarantees for predicting individual treatment effects from observational data, and shows that their framework significantly outperforms baselines in real-world experiments.
Machine learning has shown much promise in helping improve the quality of medical, legal, and financial decision-making. In these applications, machine learning models must satisfy two important criteria: (i) they must be causal, since the goal is typically to predict individual treatment effects, and (ii) they must be interpretable, so that human decision makers can validate and trust the model predictions. There has recently been much progress along each direction independently, yet the state-of-the-art approaches are fundamentally incompatible. We propose a framework for learning interpretable models from observational data that can be used to predict individual treatment effects (ITEs). In particular, our framework converts any supervised learning algorithm into an algorithm for estimating ITEs. Furthermore, we prove an error bound on the treatment effects predicted by our model. Finally, in an experiment on real-world data, we show that the models trained using our framework significantly outperform a number of baselines.