2019/07/18 by Ehud Karavani, Peter Michael Bak, Peter Bak +4 · 1 voice · 1 citation
Computer Science · Mathematics · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1907.08127
10 pages, 4 figures
arxiv created 2019/07/18 · openalex publication_date 2019/07/18 · arxiv published 2019/07/18 · arxiv updated 2019/07/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The assumption of positivity in causal inference (also known as common support and co-variate overlap) is necessary to obtain valid causal estimates. Therefore, confirming it holds in a given dataset is an important first step of any causal analysis. Most common methods to date are insufficient for discovering non-positivity, as they do not scale for modern high-dimensional covariate spaces, or they cannot pinpoint the subpopulation violating positivity. To overcome these issues, we suggest to harness decision trees for detecting violations. By dividing the covariate space into mutually exclusive regions, each with maximized homogeneity of treatment groups, decision trees can be used to automatically detect subspaces violating positivity. By augmenting the method with an additional random forest model, we can quantify the robustness of the violation within each subspace. This solution is scalable and provides an interpretable characterization of the subspaces in which violations occur. We provide a visualization of the stratification rules that define each subpopulation, combined with the severity of positivity violation within it. We also provide an interactive version of the visualization that allows a deeper dive into the properties of each subspace.