2020/06/08 by Qiming Du, Du, Qiming, Gérard Biau +5 · 1 citation
Computer Science · Engineering · Environmental Science · Mathematics · #Advanced Causal Inference Techniques #Bayesian Modeling and Causal Inference #Environmental remediation with nanomaterials #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Groundwater flow and contamination studies #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Mineral Processing and Grinding
paper · pdf · doi:10.48550/arxiv.2006.04709
openalex publication_date 2020/06/08 · openalex created_date 2020/06/12 · openalex updated_date 2026/07/28
We present new insights into causal inference in the context of Heterogeneous Treatment Effects by proposing natural variants of Random Forests to estimate the key conditional distributions. To achieve this, we recast Breiman's original splitting criterion in terms of Wasserstein distances between empirical measures. This reformulation indicates that Random Forests are well adapted to estimate conditional distributions and provides a natural extension of the algorithm to multivariate outputs. Following the philosophy of Breiman's construction, we propose some variants of the splitting rule that are well-suited to the conditional distribution estimation problem. Some preliminary theoretical connections are established along with various numerical experiments, which show how our approach may help to conduct more transparent causal inference in complex situations.