Counterfactual Learning with Multioutput Deep Kernels

TL;DR

This paper introduces a novel Bayesian nonparametric approach using multioutput deep kernels for counterfactual inference in high-dimensional observational data, enabling efficient estimation of causal effects and policy learning.

Contribution

It proposes a new class of counterfactual multi-task deep kernel models that improve sample efficiency and scalability for causal inference with multiple actions and outcomes.

Findings

Effective in estimating individual causal effects

Performs well in off-policy evaluation tasks

Scalable to high-dimensional data

Abstract

In this paper, we address the challenge of performing counterfactual inference with observational data via Bayesian nonparametric regression adjustment, with a focus on high-dimensional settings featuring multiple actions and multiple correlated outcomes. We present a general class of counterfactual multi-task deep kernels models that estimate causal effects and learn policies proficiently thanks to their sample efficiency gains, while scaling well with high dimensions. In the first part of the work, we rely on Structural Causal Models (SCM) to formally introduce the setup and the problem of identifying counterfactual quantities under observed confounding. We then discuss the benefits of tackling the task of causal effects estimation via stacked coregionalized Gaussian Processes and Deep Kernels. Finally, we demonstrate the use of the proposed methods on simulated experiments that span individual causal effects estimation, off-policy evaluation and optimization.