  Positivity is one of the three conditions for causal inference from
observational data. The standard way to validate positivity is to analyze the
distribution of propensity. However, to democratize the ability to do causal
inference by non-experts, it is required to design an algorithm to (i) test
positivity and (ii) explain where in the covariate space positivity is lacking.
The latter could be used to either suggest the limitation of further causal
analysis and/or encourage experimentation where positivity is violated. The
contribution of this paper is first present the problem of automatic positivity
analysis and secondly to propose an algorithm based on a two steps process. The
first step, models the propensity condition on the covariates and then analyze
the latter distribution using multiple hypothesis testing to create positivity
violation labels. The second step uses asymmetrically pruned decision trees for
explainability. The latter is further converted into readable text a non-expert
can understand. We demonstrate our method on a proprietary data-set of a large
software enterprise.
