From Causal Pairs to Causal Graphs
This work addresses causal structure learning for researchers and practitioners, offering incremental improvements in computational efficiency and performance.
The paper tackled the problem of learning causal graphs from observational data by proposing new methods that generate a probability distribution over graphs using cause-effect pair features, resulting in statistically similar or better performance and faster computation compared to traditional approaches.
Causal structure learning from observational data remains a non-trivial task due to various factors such as finite sampling, unobserved confounding factors, and measurement errors. Constraint-based and score-based methods tend to suffer from high computational complexity due to the combinatorial nature of estimating the directed acyclic graph (DAG). Motivated by the `Cause-Effect Pair' NIPS 2013 Workshop on Causality Challenge, in this paper, we take a different approach and generate a probability distribution over all possible graphs informed by the cause-effect pair features proposed in response to the workshop challenge. The goal of the paper is to propose new methods based on this probabilistic information and compare their performance with traditional and state-of-the-art approaches. Our experiments, on both synthetic and real datasets, show that our proposed methods not only have statistically similar or better performances than some traditional approaches but also are computationally faster.