Ancestral Causal Inference
This work addresses the problem of unreliable causal inference from small datasets for researchers in fields like biology, though it appears incremental as it builds on existing redundancy-based approaches.
The paper tackles the challenge of constraint-based causal discovery from limited data by introducing a novel method that reduces combinatorial search space and scores causal predictions for improved accuracy and scalability, achieving speedups of several orders of magnitude on synthetic data.
Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the reliability of the predictions by exploiting redundancy in the independence information have been proposed recently. Though promising, existing approaches can still be greatly improved in terms of accuracy and scalability. We present a novel method that reduces the combinatorial explosion of the search space by using a more coarse-grained representation of causal information, drastically reducing computation time. Additionally, we propose a method to score causal predictions based on their confidence. Crucially, our implementation also allows one to easily combine observational and interventional data and to incorporate various types of available background knowledge. We prove soundness and asymptotic consistency of our method and demonstrate that it can outperform the state-of-the-art on synthetic data, achieving a speedup of several orders of magnitude. We illustrate its practical feasibility by applying it on a challenging protein data set.