Y0工具包可自动判断因果关系是否可识别，助力研究者精准设计分析方案。
Causal identification with $Y_0$
- 基于符号化表达与图模型，自动判断因果效应能否从数据中识别
- 支持随机试验、观察研究等混合数据源的因果推断查询
- 适合需要验证因果可识别性的研究人员，尤其适用于复杂混杂场景
我们提出 Y0 Python 工具包，实现干预、反事实与可迁移性查询的因果识别算法，适用于随机对照试验、观察性研究或其混合数据。Y0聚焦因果关系的定性可识别性判断，帮助研究者在估算因果强度前确认其是否可被估计。该工具提供领域特定语言，用于表示因果查询与可估计量为符号化概率表达式，支持含未观测混杂的因果图模型（如 ADMGs），并实现了近期因果推断文献中的多个识别算法。Y0 源码开源于 MIT 许可协议，可通过 pip install y0 安装。
原文摘要 · Abstract (English)
We present the $Y_0$ Python package, which implements causal identification algorithms that apply interventional, counterfactual, and transportability queries to data from (randomized) controlled trials, observational studies, or mixtures thereof. $Y_0$ focuses on the qualitative investigation of causation, helping researchers determine whether a causal relationship can be estimated from available data before attempting to estimate how strong that relationship is. Furthermore, $Y_0$ provides guidance on how to transform the causal query into a symbolic estimand that can be non-parametrically estimated from the available data. $Y_0$ provides a domain-specific language for representing causal queries and estimands as symbolic probabilistic expressions, tools for representing causal graphical models with unobserved confounders, such as acyclic directed mixed graphs (ADMGs), and implementations of numerous identification algorithms from the recent causal inference literature. The $Y_0$ source code can be found under the MIT License at https://github.com/y0-causal-inference/y0 and it can be installed with pip install y0.
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