摘要翻译：
除了对治疗效果进行有效的统计估计之外，因果推论的成功应用还需要对观察到的数据背后的机制做出具体假设，并测试它们是否有效，以及在多大程度上有效。然而，大多数用于因果推理的库只专注于提供强大的统计估计器的任务。我们描述了DoWhy，这是一个开源的Python库，它是以因果假设作为其第一类公民构建的，它基于因果图的正式框架来指定和测试因果假设。DoWhy提出了一个API，用于任何因果分析的四个常见步骤--1）使用因果图和结构假设对数据建模，2)识别期望的效果在因果模型下是否可估计，3)使用统计估计器估计效果，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实现了许多健壮性检查，包括安慰剂测试、引导测试和未加注意的混杂测试。DoWhy是一个可扩展的库，它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
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英文标题：
《DoWhy: An End-to-End Library for Causal Inference》
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作者：
Amit Sharma, Emre Kiciman
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最新提交年份：
2020
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分类信息：

一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Mathematical Software 数学软件
分类描述：Roughly includes material in ACM Subject Class G.4.
大致包括ACM学科类G.4的材料。
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
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
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