  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.
