Causal inference via algebraic geometry: feasibility tests for functional causal structures with two binary observed variables

Understand

We provide a scheme for inferring causal relations from uncontrolled statistical data based on tools from computational algebraic geometry, in particular, the computation of Groebner bases.

• We focus on causal structures containing just two observed variables, each of which is binary.
• We consider the consequences of imposing different restrictions on the number and cardinality of latent variables and of assuming different functional dependences of the observed variables on the latent ones (in particular, the noise need not be additive).
• We provide an inductive scheme for classifying functional causal structures into distinct observational equivalence classes.