Causal Relational Learning
Causal Relational Learning
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DOI：
10.1145/3318464.3389759
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Babak Salimi;Harsh Parikh;Moe Kayali;Sudeepa Roy;L. Getoor;Dan Suciu
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文献类型：
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作者：
Babak Salimi;Harsh Parikh;Moe Kayali;Sudeepa Roy;L. Getoor;Dan Suciu
因果推理是自然科学和社会科学实证研究的核心，对于科学发现和知情决策至关重要。因果推理的黄金标准是进行随机对照试验;不幸的是，由于伦理、法律的或成本的限制，这些试验并不总是可行的。作为一种替代办法，统计研究和社会科学已经发展出从观测数据进行因果推断的方法。然而，现有的方法严重依赖于限制性的假设，如研究人口组成的同质元素，可以表示在一个单一的平面表，其中每一行被称为一个单位。相比之下，在许多现实环境中，研究领域自然由具有复杂关系结构的异构元素组成，其中数据自然表示在多个相关表中。在本文中，我们提出了一个正式的框架，因果关系的数据推断。我们提出了一个声明性的语言CARL捕获因果背景知识和假设，并指定因果查询使用简单的数据日志一样的规则。CARL提供了一个基础，推断因果关系和推理的影响，复杂的干预措施在关系领域。我们提出了一个广泛的实验评估真实的关系数据，以说明CARL在社会科学和医疗保健的适用性。
Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making. The gold standard in causal inference is performing randomized controlled trials ; unfortunately these are not always feasible due to ethical, legal, or cost constraints. As an alternative, methodologies for causal inference from observational data have been developed in statistical studies and social sciences. However, existing methods critically rely on restrictive assumptions such as the study population consisting of homogeneous elements that can be represented in a single flat table, where each row is referred to as a unit. In contrast, in many real-world settings, the study domain naturally consists of heterogeneous elements with complex relational structure, where the data is naturally represented in multiple related tables. In this paper, we present a formal framework for causal inference from such relational data. We propose a declarative language called CARL for capturing causal background knowledge and assumptions, and specifying causal queries using simple Datalog-like rules. CARL provides a foundation for inferring causality and reasoning about the effect of complex interventions in relational domains. We present an extensive experimental evaluation on real relational data to illustrate the applicability of CARL in social sciences and healthcare.