Active learning of causal probability trees
This work addresses the challenge of applying causal probability trees more broadly by improving learning efficiency with constrained resources, representing an incremental advance in causal machine learning.
The paper tackles the problem of learning causal probability trees from limited interventional data by introducing a Bayesian method that selects interventions with the highest expected information gain, demonstrating efficiency on simulated and real data.
The past two decades have seen a growing interest in combining causal information, commonly represented using causal graphs, with machine learning models. Probability trees provide a simple yet powerful alternative representation of causal information. They enable both computation of intervention and counterfactuals, and are strictly more general, since they allow context-dependent causal dependencies. Here we present a Bayesian method for learning probability trees from a combination of interventional and observational data. The method quantifies the expected information gain from an intervention, and selects the interventions with the largest gain. We demonstrate the efficiency of the method on simulated and real data. An effective method for learning probability trees on a limited interventional budget will greatly expand their applicability.