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2. Learning Relational Causal Models with Cycles through Relational Acyclification

   https://doi.org/10.1609/aaai.v37i10.26434  

   Ahsan, Ragib; Arbour, David; Zheleva, Elena (June 2023, Proceedings of the AAAI Conference on Artificial Intelligence)

   In real-world phenomena which involve mutual influence or causal effects between interconnected units, equilibrium states are typically represented with cycles in graphical models. An expressive class of graphical models, relational causal models, can represent and reason about complex dynamic systems exhibiting such cycles or feedback loops. Existing cyclic causal discovery algorithms for learning causal models from observational data assume that the data instances are independent and identically distributed which makes them unsuitable for relational causal models. At the same time, causal discovery algorithms for relational causal models assume acyclicity. In this work, we examine the necessary and sufficient conditions under which a constraint-based relational causal discovery algorithm is sound and complete for cyclic relational causal models. We introduce relational acyclification, an operation specifically designed for relational models that enables reasoning about the identifiability of cyclic relational causal models. We show that under the assumptions of relational acyclification and sigma-faithfulness, the relational causal discovery algorithm RCD is sound and complete for cyclic relational models. We present experimental results to support our claim. 

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3. Relational Diagrams and the Pattern Expressiveness of Relational Languages

   https://doi.org/10.1145/3733620.3733637  

   Gatterbauer, Wolfgang; Dunne, Cody (April 2025, ACM SIGMOD Record)

   Comparing relational languages by their logical expressiveness is well understood. Less understood is how to compare relational languages by their ability to represent relational query patterns. Indeed, what are query patterns other than ''a certain way of writing a query''? And how can query patterns be defined across procedural and declarative languages, irrespective of their syntax? Our SIGMOD 2024 paper proposes a semantic definition of relational query patterns that uses a variant of structure-preserving mappings between the relational tables of queries. This formalism allows us to analyze the relative pattern expressiveness of relational languages. Notably, for the nondisjunctive language fragment, we show that relational calculus (RC) can express a larger class of patterns than the basic operators of relational algebra (RA). We also propose Relational Diagrams, a complete and sound diagrammatic representation of safe relational calculus. These diagrams can represent all query patterns for unions of non-disjunctive queries, in contrast to visual query representations that derive visual marks from the basic operators of algebra. Our anonymously preregistered user study shows that Relational Diagrams allow users to recognize relational patterns meaningfully faster and more accurately than they can with SQL. 

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4. Relational Symbolic Execution

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5. Causal Relational Learning

   https://doi.org/10.1145/3318464.3389759  

   Salimi, Babak; Parikh, Harsh; Kayali, Moe; Getoor, Lise; Roy, Sudeepa; Suciu, Dan (May 2020, SIGMOD)

   Causal inference is at the heart of empirical research in natu- ral and social sciences and is critical for scientific discovery and informed decision making. The gold standard in causal inference is performing randomized controlled trials; unfortu- nately 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 sta- tistical studies and social sciences. However, existing meth- ods 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 set- tings, 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 infer- ring 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. 

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