论文标题
Dowhy：因果推理的端到端库
DoWhy: An End-to-End Library for Causal Inference
论文作者
论文摘要
除了有效的治疗效果统计估计量外，因果推理的成功应用还需要指定有关观察到的数据和测试它们是否有效的机制的假设。但是，大多数因果推论的图书馆仅关注提供强大的统计估计器的任务。我们根据因果图的正式框架来指定和检验因果假设的正式框架，以因果假设为其一流的公民Dowhy，它是由因果假设构建的。 Dowhy为任何因果分析共有的四个步骤提供了API - 1）使用因果图和结构假设对数据进行建模，2）确定在因果模型下是否可以估计所需效应，3）使用统计估计器估算效果，并最终通过鲁棒性检查获得的估计4），通过鲁棒性检查和强度估算。特别是，Dowhy实施了许多鲁棒性检查，包括安慰剂测试，自举测试和未携带混杂的测试。 Dowhy是一个可扩展的库，支持与其他实现的互操作性，例如ECONML和CAUSALML为估计步骤。该库可从https://github.com/microsoft/dowhy获得
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step. The library is available at https://github.com/microsoft/dowhy
