Demonstration of inferring causality from relational databases with CaRL

Abstract

Understanding cause-and-effect is key for informed decision-making. The gold standard in causal inference is performing controlled experiments, which may not always be feasible due to ethical, legal, or cost constraints. As an alternative, inferring causality from observational data has been extensively used in statistics and social sciences. However, the existing methods critically rely on a restrictive assumption that the population of study consists of homogeneous units that can be represented as a single flat table. In contrast, in many real-world settings, the study domain consists of heterogeneous units that are best represented using relational databases. We propose and demonstrate CaRL: an end-to-end system for drawing causal inference from relational data. In addition, we built a visual interface to wrap around CaRL. In our demonstration, we will use this GUI to show a live investigation of causal inference from real academic and medical relational databases.

Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships

“…Recent work in the database community helps researchers answer causal questions about multilevel, or hierarchical, data [28,51]. CaRL [51] provides a domain-specific language to express causal relationships between variables and a GUI to show researchers results.…”