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

TL;DR

This paper introduces an algebraic geometry-based method for inferring causal relations between two binary variables, providing a systematic way to classify structures and determine model constraints from data.

Contribution

It develops a novel algebraic geometry framework using Groebner bases to classify and identify causal structures with binary variables, including latent factors and functional dependencies.

Findings

Classifies causal structures into observational equivalence classes.

Provides necessary and sufficient conditions for observed data to fit a causal model.

Demonstrates how to identify and solve for causal parameters.

Abstract

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. For each observational equivalence class, we provide a procedure for deriving constraints on the joint distribution that are necessary and sufficient conditions for it to arise from a model in that class. We also demonstrate how this sort of approach provides a means of determining which causal parameters are identifiable and how to solve for these. Prospects for expanding the scope of our scheme, in particular to the problem of quantum causal inference, are also discussed.