  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.
