Algorithmic Causal Effect Identification with causaleffect
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使用causaleffect进行算法化因果效应识别
- 研究问题
- 如何根据因果图和可用概率信息，用算法判断条件或无条件因果查询是否可识别，并输出相应的概率表达式？
- 方法与思路
- 作者介绍概率、图论与do演算基础，研究Shpitser与Pearl的识别算法，并说明Python实现及库的使用示例。
- 材料陈述的贡献
- 摘要称报告实现了因果查询识别算法并开发Python库，算法可返回查询表达式，或在相应框架下报告不可识别。
- 阅读关注点
- 应核对算法允许的图结构与潜在混杂、输入概率类型，以及条件查询处理、失败判据和代码实现是否对应原算法。
- 阅读边界
- 这是arXiv预印本，同行评审与期刊接收未核实；摘要同时提及观察数据与实验概率，输入信息范围需全文确认。
原文摘要
Our evolution as a species made a huge step forward when we understood the relationships between causes and effects. These associations may be trivial for some events, but they are not in complex scenarios. To rigorously prove that some occurrences are caused by others, causal theory and causal inference were formalized, introducing the $do$-operator and its associated rules. The main goal of this report is to review and implement in Python some algorithms to compute conditional and non-conditional causal queries from observational data. To this end, we first present some basic background knowledge on probability and graph theory, before introducing important results on causal theory, used in the construction of the algorithms. We then thoroughly study the identification algorithms presented by Shpitser and Pearl in 2006, explaining our implementation in Python alongside. The main identification algorithm can be seen as a repeated application of the rules of $do$-calculus, and it eventually either returns an expression for the causal query from experimental probabilities or fails to identify the causal effect, in which case the effect is non-identifiable. We introduce our newly developed Python library and give some usage examples.