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

中文翻译：

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DoWhy：因果推理的端到端库

除了治疗效果的有效统计估计之外，因果推断的成功应用还需要指定关于所观察数据背后的机制的假设，并测试它们是否有效，以及在多大程度上有效。然而，大多数因果推理库只关注提供强大的统计估计器的任务。我们描述了 DoWhy，这是一个开源 Python 库，它以因果假设作为其一等公民，基于因果图的正式框架来指定和测试因果假设。DoWhy 为任何因果分析共有的四个步骤提供了一个 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的效果，3) 使用统计估计效果估计量，最后 4) 通过稳健性检查和敏感性分析反驳所获得的估计。特别是，DoWhy 实施了许多稳健性检查，包括安慰剂测试、引导程序测试和未观察到的混杂测试。DoWhy 是一个可扩展库，支持与其他实现的互操作性，例如用于估计步骤的 EconML 和 CausalML。该库可在 https://github.com/microsoft/dowhy 获得