Keywords: Causal estimation, Robust prediction, Optimization, Directional derivative, Interventional data
TL;DR: Based on multi-environment data, we propose an optimization algorithm that can accurately estimate the causal effects under spurious association and produce distributionally robust predictions.
Abstract: This paper presents an optimization approach to causal estimation. In classical machine learning, the goal of optimization is to maximize predictive accuracy. However, some covariates might exhibit non-causal association to the outcome. Such spurious associations provide predictive power for classical ML, but prevent us from interpreting the result causally. This paper proposes CoCo, an optimization algorithm that bridges the gap between pure prediction and causal inference. CoCo leverages the recently-proposed idea of environments. Given datasets from multiple environments---and ones that exhibit enough heterogeneity---CoCo maximizes an objective for which the only solution is the causal solution. We describe the theoretical foundations of this approach and demonstrate its effectiveness on simulated and real datasets. Compared to classical ML and the recently-proposed IRMv1, CoCo provides more accurate estimates of the causal model.