Learning Subgroups with Maximum Treatment Effects without Causal Heuristics
This work addresses a crucial issue in precision medicine, public policy, and education by providing a more accurate method for subgroup discovery without relying on ad-hoc causal heuristics, though it is incremental as it builds on existing partition-based methods.
The paper tackles the problem of discovering subgroups with maximum average treatment effects for targeted decision-making, showing that under a structural causal model framework, optimal subgroup discovery reduces to a standard supervised learning problem, and their method using CART more accurately identifies these subgroups compared to baselines on synthetic and semi-synthetic datasets.
Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While most prior work is formulated in the potential outcome framework, the corresponding structural causal model (SCM) for this task has been largely overlooked. In practice, two approaches dominate. The first estimates pointwise conditional treatment effects and then fits a tree on those estimates, effectively turning subgroup estimation into the harder problem of accurate pointwise estimation. The second constructs decision trees or rule sets with ad-hoc 'causal' heuristics, typically without rigorous justification for why a given heuristic may be used or whether such heuristics are necessary at all. We address these issues by studying the problem directly under the SCM framework. Under the assumption of a partition-based model, we show that optimal subgroup discovery reduces to recovering the data-generating models and hence a standard supervised learning problem (regression or classification). This allows us to adopt any partition-based methods to learn the subgroup from data. We instantiate the approach with CART, arguably one of the most widely used tree-based methods, to learn the subgroup with maximum treatment effect. Finally, on a large collection of synthetic and semi-synthetic datasets, we compare our method against a wide range of baselines and find that our approach, which avoids such causal heuristics, more accurately identifies subgroups with maximum treatment effect. Our source code is available at https://github.com/ylincen/causal-subgroup.