Causal Relational Learning

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.

References in corpus (6)

• Homophily and Contagion Are Generically Confounded in Observational Social Network Studies
• For objective causal inference, design trumps analysis
• Reasoning about Independence in Probabilistic Models of Relational Data
• Quantifying Causal Effects on Query Answering in Databases
• Capuchin: Causal Database Repair for Algorithmic Fairness
• MALTS: Matching After Learning to Stretch

Cited by in corpus (2)

• DataExposer: Exposing Disconnect between Data and Systems
• Heterogeneous Treatment Effects in Social Networks