基于零分数多假设检验和非对称剪枝决策树的正性验证检测和解释
Positivity Validation Detection and Explainability via Zero Fraction Multi-Hypothesis Testing and Asymmetrically Pruned Decision Trees
摘要：
积极性是从观察数据中进行因果推理的三个条件之一。验证积极性的标准方法是分析倾向的分布。然而，为了使非专家进行因果推理的能力民主化，需要设计一种算法来（i）测试积极性和（ii）解释协变量空间中缺乏积极性的地方。后者可以用来暗示进一步因果分析的局限性和/或鼓励违反积极性的实验。本文的贡献是首先提出了自动积极性分析的问题，其次提出了一个基于两步过程的算法。第一步，对协变量的倾向条件进行建模，然后使用多个假设检验来分析后者的分布，以创建积极违规标签。第二步使用非对称修剪的决策树来实现可解释性。后者被进一步转换为非专家可以理解的可读文本。我们证明了我们的方法上的一个大型软件企业的专有数据集。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
