ParKCa: Causal Inference with Partially Known Causes

Raquel Aoki^*, Martin Ester

School of Computing Science, Simon Fraser University
^*Corresponding author
Email: raoki@sfu.ca

Pacific Symposium on Biocomputing 26:196-207(2021)

© 2021 World Scientific
Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License.

Abstract

Methods for causal inference from observational data are an alternative for scenarios where collecting counterfactual data or realizing a randomized experiment is not possible. 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.