Interpretable Deep Causal Learning for Moderation Effects

TL;DR

This paper introduces a novel deep learning architecture for causal inference that enhances interpretability and targeted regularization in estimating individual treatment effects, effectively disentangling effects and providing uncertainty quantification.

Contribution

The paper presents a new deep counterfactual learning model that improves interpretability and regularization in causal effect estimation, with disentangled effects and uncertainty quantification.

Findings

Effective disentanglement of baseline and moderating effects.

Provides interpretable score functions for covariate effects.

Demonstrated on a simulated experiment.

Abstract

In this extended abstract paper, we address the problem of interpretability and targeted regularization in causal machine learning models. In particular, we focus on the problem of estimating individual causal/treatment effects under observed confounders, which can be controlled for and moderate the effect of the treatment on the outcome of interest. Black-box ML models adjusted for the causal setting perform generally well in this task, but they lack interpretable output identifying the main drivers of treatment heterogeneity and their functional relationship. We propose a novel deep counterfactual learning architecture for estimating individual treatment effects that can simultaneously: i) convey targeted regularization on, and produce quantify uncertainty around the quantity of interest (i.e., the Conditional Average Treatment Effect); ii) disentangle baseline prognostic and moderating effects of the covariates and output interpretable score functions describing their relationship with the outcome. Finally, we demonstrate the use of the method via a simple simulated experiment.