Causal Gene Selection Using Marginal Independencies

Most causal learning algorithms, especially the ones using conditional independence tests, suffer in the low sample regime. In this paper, we examine the possibility of causal discovery using only the pairwise dependency structure of a domain and investigate if this approach can lead to more robust and reliable solutions to causal discovery when the sample size is limited. We show that marginal independence relations can be used to construct a novel causal discovery tool capable of distinguishing between direct and indirect causal effects and detecting a lower bound on direct causal effects. In our simulations we found that causal discovery using marginal independency information can be significantly more accurate than causal knowledge obtained through conditional independence tests when the sample size is limited: our experiments suggests avoiding conditional independence tests can reduce error rate by up to 30 percent. Finally, using this marginal independency based causal discovery tool, we propose four candidate genes that possibly contain the causal mutation in Major Depressive Disorder utilizing a database of only 59 samples.