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The human mind has a remarkable ability to associate causes with a specific event. From the outcome of an election to an object dropping on the floor, we are constantly associating chains of events that cause a specific effect. Neuropsychology refers to this cognitive ability as causal reasoning. Computer science and economics study a specific form of causal reasoning known as causal inference which focuses on exploring relationships between two observed variables. Over the years, machine learning has produced many methods for causal inference but they remain mostly difficult to use in mainstream applications. Recently, Microsoft Research open sourced DoWhy, a framework for causal thinking and analysis.

人脑具有将原因与特定事件相关联的卓越能力。 从选举的结果到物体掉到地板上，我们一直在关联引起特定效果的事件链。 神经心理学将这种认知能力称为因果推理。 计算机科学和经济学研究因果推理的一种特定形式，即因果推理，该因果推理着重探讨两个观察到的变量之间的关系。 多年来，机器学习已经产生了许多因果推理方法，但在主流应用中仍然很难使用。 最近，Microsoft Research 开源了DoWhy ，这是一个因果思维和分析框架。

The challenge with causal inference is not that is a new discipline, quite the opposite, but that the current methods represent a very small and simplistic version of causal reasoning. Most models that try to connect causes such as linear regression rely on empirical analysis that makes some assumption about the data. Pure causal inference relies on counterfactual analysis which is a closer representation to how humans make decisions. Imagine a scenario in which you are traveling with your families for vacations to an unknown destination. Before and after the vacation you are wrestling with a few counterfactual questions:

因果推理的挑战不是这是一门新学科，而是相反，而是当前的方法代表了因果推理的非常小而简单的版本。 大多数尝试联系起因(例如线性回归)的模型都依赖于对数据进行一些假设的经验分析。 纯粹的因果推论依赖于反事实分析，该反事实分析更接近于人类如何做出决策。 设想一下您与家人一起旅行到未知目的地度假的情况。 假期前后，您会遇到一些反事实问题：

Answering these questions is the focus of causal inference. Unlike supervised learning, causal inference depends on estimation of unobserved quantities. This if often known as the “fundamental problem” of causal inference which implies that a model never has a purely objective evaluation through a held-out test set. In our vacation example, you can either observe the effects on going on vacation or not going on vacations but never both. This challenge forces causal inference to make critical assumptions about the data generation process. Traditional machine learning frameworks for causal inference try to take shortcuts around the “fundamental problem” resulting on a very frustrating experience for data scientists and developers.

回答这些问题是因果推理的重点。 与监督学习不同，因果推理取决于未观察到的量的估计。 这通常被称为因果推理的“基本问题”，这意味着模型永远不会通过坚持的测试集进行纯粹客观的评估。 在我们的休假示例中，您可以观察休假或不休假的影响，但不能同时观察两者。 这一挑战迫使因果推理对数据生成过程做出重要假设。 传统的因果推理机器学习框架试图绕开“基本问题”的捷径，从而给数据科学家和开发人员带来非常令人沮丧的体验。

介绍DoWhy (Introducing DoWhy)

Microsoft’s DoWhy is a Python-based library for causal inference and analysis that attempts to streamline the adoption of causal reasoning in machine learning applications. Inspired by Judea Pearl’s do-calculus for causal inference, DoWhy combines several causal inference methods under a simple programming model that removes many of the complexities of traditional approaches. Compared to its predecessors, DoWhy makes three key contributions to the implementation of causal inference models.

微软的DoWhy 是一个基于Python的因果推理和分析库，旨在简化因果推理在机器学习应用程序中的采用。 受Judea Pearl的因果推理do-演算的启发，DoWhy在简单的编程模型下结合了多种因果推理方法，从而消除了传统方法的许多复杂性。 与以前的版本相比，DoWhy对因果推理模型的实现做出了三个关键贡献。

1. Provides a principled way of modeling a given problem as a causal graph so that all assumptions explicit.提供将给定问题建模为因果图的原则方法，以便所有假设都明确。
2. Provides a unified interface for many popular causal inference methods, combining the two major frameworks of graphical models and potential outcomes.为许多流行的因果推断方法提供统一的界面，将图形模型和潜在结果的两个主要框架结合在一起。
3. Automatically tests for the validity of assumptions if possible and assesses robustness of the estimate to violations.如果可能，自动测试假设的有效性，并评估对违规情况的估计的稳健性。

Conceptually, DoWhy was created following two guiding principles: asking causal assumptions explicit and testing robustness of the estimates to violations of those assumptions. In other words, DoWhy separates the identification of a causal effect from the estimation of its relevance which enables the inference of very sophisticated causal relationships.

从概念上讲，DoWhy的创建遵循两个指导原则：明确提出因果假设，并测试估计值对违反那些假设的稳健性。 换句话说，DoWhy将因果效应的标识与对它的相关性的估计分开，从而可以推断出非常复杂的因果关系。

To accomplish its goal, DoWhy models any causal inference problem in a workflow with four fundamental steps: model, identify, estimate and refute.

为了实现其目标，DoWhy通过四个基本步骤对工作流中的任何因果推断问题进行建模：建模，识别，估计和反驳。

1. Model: DoWhy models each problem using a graph of causal relationships. The current version of DoWhy supports two formats for graph input: gml (preferred) and dot. The graph might include prior knowledge of the causal relationships in the variables but DoWhy does not make any immediate assumptions.

   模型： DoWhy使用因果关系图为每个问题建模。 当前版本的DoWhy支持两种图形输入格式： gml (首选)和dot 。 该图可能包括变量中因果关系的先验知识，但DoWhy没有立即做出任何假设。

2. Identify: Using the input graph, DoWhy finds all possible ways of identifying a desired causal effect based on the graphical model. It uses graph-based criteria and do-calculus to find potential ways find expressions that can identify the causal effect

   识别： DoWhy使用输入图，根据图形模型找到识别期望因果效应的所有可能方法。 它使用基于图的标准和do-演算来找到潜在的方式来找到可以识别因果关系的表达式

3. Estimate: DoWhy estimates the causal effect using statistical methods such as matching or instrumental variables. The current version of DoWhy supports estimation methods based such as propensity-based-stratification or propensity-score-matching that focus on estimating the treatment assignment as well as regression techniques that focus on estimating the response surface.

   估计： DoWhy使用统计方法(例如匹配或工具变量)来估计因果关系。 DoWhy的当前版本支持基于估计的方法，例如基于倾向的分层或倾向得分匹配，这些方法着重于估计治疗分配，以及回归技术着重于估计响应面。

4. Verify: Finally, DoWhy uses different robustness methods to verify the validity of the causal effect.

   验证：最后，DoWhy使用不同的鲁棒性方法来验证因果效应的有效性。

使用DoWhy (Using DoWhy)

Developers can start using DoWhy by installing the Python module using the following command:

通过使用以下命令安装Python模块，开发人员可以开始使用DoWhy：

python setup.py install

Like any other machine learning program, the first step of a DoWhy application is to load the dataset. In this example, imagine that we are trying to infer the correlation between different medical treatments and outcomes represented by the following dataset.

像任何其他机器学习程序一样，DoWhy应用程序的第一步是加载数据集。 在此示例中，假设我们正在尝试推断不同药物治疗与以下数据集所表示的结果之间的相关性。

Treatment    Outcome        w00   2.964978   5.858518 -3.1733991   3.696709   7.945649 -1.9369952   2.125228   4.076005 -3.9755663   6.635687  13.471594  0.7724804   9.600072  19.577649  3.922406

DoWhy relies on pandas dataframes to capture the input data:

DoWhy依靠熊猫数据框来捕获输入数据：

rvar = 1 if np.random.uniform() >0.5 else 0 data_dict = dowhy.datasets.xy_dataset(10000, effect=rvar, sd_error=0.2) df = data_dict['df']print(df[["Treatment", "Outcome", "w0"]].head())

At this point, we simply need about four steps to infer causal relationships between the variables. The four steps correspond to the four operations of DoWhy: model, estimate, infer and refute. We can start by modeling the problem as a causal graph:

在这一点上，我们只需要大约四个步骤就可以推断变量之间的因果关系。 这四个步骤与DoWhy的四个操作相对应：建模，估计，推断和反驳。 我们可以先将问题建模为因果图：

model= CausalModel(        data=df,        treatment=data_dict["treatment_name"],        outcome=data_dict["outcome_name"],        common_causes=data_dict["common_causes_names"],        instruments=data_dict["instrument_names"])model.view_model(layout="dot")from IPython.display import Image, displaydisplay(Image(filename="causal_model.png"))

The next step is to identify the causal relationships in the graph:

下一步是确定图中的因果关系：

identified_estimand = model.identify_effect()

Now we can estimate the causal effect and determine if the estimation is correct. This example uses linear regression for simplicity:

现在我们可以估计因果关系并确定估计是否正确。 为了简单起见，本示例使用线性回归：

estimate = model.estimate_effect(identified_estimand,        method_name="backdoor.linear_regression")# Plot Slope of line between treamtent and outcome =causal effectdowhy.plotter.plot_causal_effect(estimate, df[data_dict["treatment_name"]], df[data_dict["outcome_name"]])

Finally, we can use different technique to refute the causal estimator:

最后，我们可以使用不同的技术来反驳因果估计量：

res_random=model.refute_estimate(identified_estimand, estimate, method_name="random_common_cause")

DoWhy is a very simple and useful framework to implement causal inference models. The current version can be used as a standalone library or integrated into popular deep learning frameworks such as TensorFlow or PyTorch. The combination of multiple causal inference methods under a single framework and the four-step simple programming model makes DoWhy incredibly simple to use for data scientist tackling causal inference problems.

DoWhy是一个非常简单且有用的框架，用于实现因果推理模型。 当前版本可以用作独立库，也可以集成到流行的深度学习框架(如TensorFlow或PyTorch)中。 在一个框架下结合多种因果推断方法和四步简单编程模型，使得DoWhy极其简单地用于数据科学家解决因果推断问题。