A Recursive Markov Boundary-Based Approach to Causal Structure Learning
This work addresses a scalability problem for researchers and practitioners in causal inference, offering an incremental improvement over existing constraint-based methods.
The paper tackles the computational inefficiency of constraint-based causal structure learning by proposing a recursive method that uses Markov boundary information to reduce the number of required conditional independence tests, achieving a lower bound and outperforming state-of-the-art methods in experiments.
Constraint-based methods are one of the main approaches for causal structure learning that are particularly valued as they are asymptotically guaranteed to find a structure that is Markov equivalent to the causal graph of the system. On the other hand, they may require an exponentially large number of conditional independence (CI) tests in the number of variables of the system. In this paper, we propose a novel recursive constraint-based method for causal structure learning that significantly reduces the required number of CI tests compared to the existing literature. The idea of the proposed approach is to use Markov boundary information to identify a specific variable that can be removed from the set of variables without affecting the statistical dependencies among the other variables. Having identified such a variable, we discover its neighborhood, remove that variable from the set of variables, and recursively learn the causal structure over the remaining variables. We further provide a lower bound on the number of CI tests required by any constraint-based method. Comparing this lower bound to our achievable bound demonstrates the efficiency of the proposed approach. Our experimental results show that the proposed algorithm outperforms state-of-the-art both on synthetic and real-world structures.