Causal Relational Learning

TL;DR

This paper introduces CaRL, a formal framework and declarative language for causal inference in complex relational data, enabling reasoning about causality beyond traditional flat-table assumptions.

Contribution

It presents CaRL, a novel declarative language for causal inference in relational data, extending causal reasoning to heterogeneous, multi-table domains.

Findings

CaRL effectively models causal queries in relational datasets.

Experimental results demonstrate CaRL's applicability in social sciences and healthcare.

CaRL outperforms existing methods in complex relational causal inference.

Abstract

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.