source: arxiv statistics ml: integrating background knowledge for scalable causal discovery
level: research
expert background knowledge is often available in real-world causal discovery tasks. such constraints can improve identifiability of causal effects and accuracy of learned structures. they also reduce the search space of possible causal graphs. this is especially important when dealing with many variables, where causal discovery can become computationally expensive. most current methods only apply background knowledge after the discovery process to refine the graph, missing opportunities for efficiency gains.
this work introduces a framework that uses background knowledge during the causal discovery process itself. it focuses on scalable methods that recover only a subset of the full graph. the framework is implemented for multiple algorithms. empirical results show that incorporating background knowledge early can speed up computation and improve results. the approach avoids the common postprocessing step, integrating constraints directly into the search.
the framework is designed for practical applications where expert knowledge is available. by reducing the candidate graph space upfront, it makes causal discovery feasible for larger datasets. the method is tested on several algorithms, demonstrating consistent benefits. this work highlights the importance of using all available information during learning, not just after. it provides a path toward more efficient and accurate causal modeling in data science.
why it matters: it enables faster and more accurate causal discovery in large-scale data science projects by using expert knowledge during learning, not just after.
source: arxiv statistics ml: integrating background knowledge for scalable causal discovery