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Author ORCID Identifier



Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded


Month Degree Awarded


First Advisor

David Jensen

Second Advisor

Ben Marlin

Third Advisor

Dan Sheldon

Fourth Advisor

Nick Reich

Subject Categories

Artificial Intelligence and Robotics


The analysis of data from complex systems is quickly becoming a fundamental aspect of modern business, government, and science. The field of causal learning is concerned with developing a set of statistical methods that allow practitioners make inferences about unseen interventions. This field has seen significant advances in recent years. However, the vast majority of this work assumes that data instances are independent, whereas many systems are best described in terms of interconnected instances, i.e. relational systems. This discrepancy prevents causal inference techniques from being reliably applied in many real-world settings.
In this thesis, I will present three contributions to the field of causal inference that seek to enable the analysis of relational systems. First, I will present theory for consistently testing statistical dependence in relational domains. I then show how the significance of this test can be measured in practice using a novel bootstrap method for structured domains. Second, I show that statistical dependence in relational domains is inherently asymmetric, implying a simple test of causal direction from observational data. This test requires no assumptions on either the marginal distributions of variables or the functional form of dependence. Third, I describe relational causal adjustment, a procedure to identify the effects of arbitrary interventions from observational relational data via an extension of Pearl's backdoor criterion. A series of evaluations on synthetic domains shows the estimates obtained by relational causal adjustment are close to those obtained from explicit experimentation.