We present a sound and complete calculus for causal relevance that uses Pearl's functional causal models as semantics. The calculus consists of axioms and rules of inference ...
This paper studies the feasibility and interpretation of learning the causal structure from observational data with the principles behind the Kolmogorov Minimal Sufficient Statist...
We use graphical models to explore the question of how people learn simple causal relationships from data. The two leading psychological theories can both be seen as estimating th...
We present an algorithm name cSAT+ for learning the causal structure in a domain from datasets measuring different variable sets. The algorithm outputs a graph with edges correspo...
Sofia Triantafilou, Ioannis Tsamardinos, Ioannis G...
: This review presents empirical researchers with recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional stati...