# Positivity Validation Detection and Explainability via Zero Fraction Multi-Hypothesis Testing and Asymmetrically Pruned Decision Trees

arXiv: https://arxiv.org/abs/2111.04033 · HTML: https://mlsanity.com/paper/2111.04033/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 7 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition](https://mlsanity.com/paper/1707.02641/) (2017)
- [An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference](https://mlsanity.com/paper/1906.00442/) (2019)
- [Propensity score weighting under limited overlap and model misspecification](https://mlsanity.com/paper/2006.04038/) (2020)
- [A discriminative approach for finding and characterizing positivity violations using decision trees](https://mlsanity.com/paper/1907.08127/) (2019)

## Built on by (most cited first, 1 of 1)

- [Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation](https://mlsanity.com/paper/2401.17737/) (2024)

## Abstract (arXiv)

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.