A Bayesian Approach to Constraint Based Causal Inference

Understand

We target the problem of accuracy and robustness in causal inference from finite data sets.

• Some state-of-the-art algorithms produce clear output complete with solid theoretical guarantees but are susceptible to propagating erroneous decisions, while others are very adept at handling and representing uncertainty, but need to rely on undesirable assumptions.
• Our aim is to combine the inherent robustness of the Bayesian approach with the theoretical strength and clarity of constraint-based methods.
• We use a Bayesian score to obtain probability estimates on the input statements used in a constraint-based procedure.