6 papers
Generalised Transportability via Causal Abstractions
Yorgos Felekis, Paris Giampouras, Fabio Massimo Zennaro +1
Transporting a causal conclusion from a source study population to a target one is a fundamental problem in causal inference. The theory of transportability provides a criterion fo…
Teleological Inference in Structural Causal Models via Intentional Interventions
Dario Compagno, Fabio Massimo Zennaro
Structural causal models (SCMs) were conceived to formulate and answer causal questions. This paper shows that SCMs can also be used to formulate and answer teleological questions,…
Multi-Level Causal Embeddings
Willem Schooltink, Fabio Massimo Zennaro
Abstractions of causal models allow for the coarsening of models such that relations of cause and effect are preserved. Whereas abstractions focus on the relation between two model…
Using causal abstractions to accelerate decision-making in complex bandit problems
Joel Dyer, Nicholas Bishop, Anisoara Calinescu +2
Although real-world decision-making problems can often be encoded as causal multi-armed bandits (CMABs) at different levels of abstraction, a general methodology exploiting the inf…
Causal Abstraction Learning based on the Semantic Embedding Principle
Gabriele D'Acunto, Fabio Massimo Zennaro, Yorgos Felekis +1
Structural causal models (SCMs) allow us to investigate complex systems at multiple levels of resolution. The causal abstraction (CA) framework formalizes the mapping between high-…
Aligning Graphical and Functional Causal Abstractions
Willem Schooltink, Fabio Massimo Zennaro
Causal abstractions allow us to relate causal models on different levels of granularity. To ensure that the models agree on cause and effect, frameworks for causal abstractions def…