Preventing spurious interactions in tree-based metalearners

Roger Pros, Jordi Vitria

Keywords: causal inference, metalearners, CATE estimation

Abstract:

In recent years, various insights have been employed to enhance causal machine learning methods by refining estimation techniques and introducing robust algorithms that account for causal structures and dependencies within the data. Building on this trend, we propose a novel method to improve the estimation of the Conditional Average Treatment Effect (CATE). A common approach in CATE estimation involves the use of metalearners, which can estimate CATE if certain identification properties are met. However, this approach employs causal knowledge only for identifying the estimand, not for the estimation process itself.

We present a new method that utilizes causal knowledge in the estimation phase by imposing variable interaction constraints during model training. These constraints are based on total or partial knowledge about the underlying data-generating process. By applying these constraints to traditional tree-based estimation algorithms, we show that models trained in this manner achieve improved performance and reduced variability in estimating CATE.

Submission Number: 15