DoWhy: An End-to-End Library for Causal Inference
Amit Sharma, Emre Kıcıman Microsoft Research
Abstract
Many questions in data science are fundamentally causal questions, such as the impact of a marketing campaign or a new product feature, the reasons for customer churn, which drug may work best for which patient, and so on. As the field of data science has grown, many practitioners are realizing the value of causal inference in providing insights from data. However, unlike the streamlined experience for supervised machine learning with libraries like Tensorflow ([1]) and PyTorch ([8]), it is non-trivial to build a causal inference analysis. Software libraries that implement state-of-the art causal inference methods can accelerate the adoption of causal inference among data analysts in both industry and academia.
中文速览
数据科学中大量问题本质上是因果问题，但现有的因果推断库大多只关注"估计"这一个环节，让分析者自己搞定建模假设和稳健性验证，门槛很高。DoWhy 针对这一痛点，将因果分析拆解为"建模—识别—估计—反驳"四个步骤，用统一的 API 串联起来，强制分析者显式声明假设、自动运行多种稳健性检验（如安慰剂处理、添加随机混杂变量、数据子集验证等），并可与 EconML、CausalML 等估计库无缝对接。在实际使用中，完成一套完整的因果分析只需四行核心代码，大幅降低了上手难度。这项工作的重要性在于，它把因果推断的最佳实践系统化、工具化，让不熟悉因果方法的数据科学家也能规范地完成从问题建模到结论验证的全流程分析。
原文 arXiv:2011.04216;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2011.04216v1