III: Medium: Collaborative Research: A Unified and Declarative Approach to Causal Analysis for Big Data

今天的观测数据以多关系的形式提供，通常从各种来源提取，并存储在多个平面和相互关联的表中。对观测数据进行因果推理的标准统计方法假设一个非常简单的数据模型：一个具有独立单元的单表。这项研究有可能对区分因果关系和相关性至关重要的应用领域产生重大影响，例如教育政策和癌症基因组学。HUME项目开发了使用声明性方法对复杂视图和从不同数据源集成的大型数据集进行有效因果分析的技术。HUME使用类似sql的语言，并与关系数据库系统集成。该项目开发了定义任意复杂单元、治疗、结果和协变量的技术，通过结合多个表的连接、数据映射和聚合，并使用因果网络选择一组良好的协变量进行因果推理。该项目的第一部分开发了可扩展的技术，用于通过声明式集成多个数据源获得的大型数据集的子分类和匹配。项目的第二部分开发可扩展的方法，通过基于约束的、基于搜索的和混合的发现过程来发现视图中属性之间的因果关系。最后，该项目的第三部分通过设计范式和利用数据库理论技术对潜在假设进行自动推断，调查了复杂视图中产生的单元之间的干扰。

Observational data is available today in multi-relational form, often extracted from various sources, and stored in multiple flat and interrelated tables. Standard statistical methods for conducting causal inference on observational data assume a very simple data model: a single table with independent units. This research has the potential to significantly impact application domains where differentiating causality from correlation is essential, e.g., education policy and cancer genomics. The HUME project develops techniques for efficient causal analysis using a declarative approach, over complex views, and over large datasets that are integrated from disparate data sources. HUME uses a SQL-like language and is integrated with a relational database system.The project develops techniques for defining arbitrarily complex units, treatments, outcomes, and covariates, by combining joins, data mapping, and aggregates across multiple tables, and uses a causal network to choose a good set of covariates for causal inference. The first part of the project develops scalable techniques for sub-classification and matching for large data sets obtained by declaratively integrating multiple data sources. The second part of the project develops scalable methods for discovering causal relationships among the attributes in the views by constraint-based, search-based, and hybrid discovery processes. Finally, the third part of the project investigates interferences among units arising from the complex views by designing normal forms and automatic inference of underlying assumptions exploiting techniques from database theory.