The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks

Directed acyclic graphs, or DAGs, are commonly used to repre sent causal relationships in complex biological systems. For example, in gene regulatory ne tworks, directed edges represent regulatory interactions among genes, which are represente d as nodes of the graph. While causal effects in biological networks can be accurately inferred fro m perturbation experiments [33]-- including single or double gene knockouts [30, 42]--these ar e costly to run. Estimating DAGs from observational data is thus an important exploratory ta sk for generating causal hypotheses [10, 15], and designing more efficient experiments. Since the number of possible directed graphs grows super-ex ponentially in the number of nodes, estimation of DAGs is an NPhard problem [6]. Methods of estimating DAGs from observational data can be broadly categorized into three cl asses. The first class, score-based methods, search over the space of all possible graphs, and at tempt to maximize a goodness-of-fit score, generally using a greedy algorithm.