Reliable causal discovery based on mutual information supremum principle for finite datasets

The recent method, MIIC (Multivariate Information-based Inductive Causation), combining constraint-based and information-theoretic frameworks, has been shown to significantly improve causal discovery from purely observational data. Yet, a substantial loss in precision has remained between skeleton and oriented graph predictions for small datasets. Here, we propose and implement a simple modification, named conservative MIIC, based on a general mutual information supremum principle regularized for finite datasets. In practice, conservative MIIC rectifies the negative values of regularized (conditional) mutual information used by MIIC to identify (conditional) independence between discrete, continuous or mixed-type variables. This modification is shown to greatly enhance the reliability of predicted orientations, for all sample sizes, with only a small sensitivity loss compared to MIIC original orientation rules. Conservative MIIC is especially interesting to improve the reliability of causal discovery for real-life observational data applications.