A Short Survey on Forest Based Heterogeneous Treatment Effect Estimation Methods: Meta-learners and Specific Models

Abstract

Causation is gradually paid more attention to in industry as compared with correlation statement, it straightly targets on answering what-if questions, which generally delivers deeper and more insightful conclusions. Therefore, causal inference is naturally called. Mainly targeting on modeling counter-factual relationship that is usually not directly observable, causal inference has various of challenges on both problem setup and modeling side, which makes it a more complex topic than regular supervised learning task. As one of the heated discussed specific causal inference problems, conditional average treatment effect (CATE), or heterogeneous treatment effect (HTE), estimation model serves as a powerful tool in many applications, like personalized medicine and a series of uplift problems from user segmentation to ads budget optimization. Recently, several new CATE methods were proposed and we would like to do a short survey from the perspective of forest-based model to cover both meta-learners that could take random forest as base learner and forest-based specific CATE models. In total, we discussed 7 meta-learners and 5 forest-based specific models. We empirically evaluate these models with both synthetic data and real dataset.