  Methods for causal inference from observational data are an alternative for
scenarios where collecting counterfactual data or realizing a randomized
experiment is not possible. Adopting a stacking approach, our proposed method
ParKCA combines the results of several causal inference methods to learn new
causes in applications with some known causes and many potential causes. We
validate ParKCA in two Genome-wide association studies, one real-world and one
simulated dataset. Our results show that ParKCA can infer more causes than
existing methods.
