Uncovering hidden causal assumptions could revolutionize discovery algorithms for networks.
The article explores how causal models can be improved to better understand cause and effect relationships. By examining current assumptions about causality, researchers aim to uncover the key principles that guide the creation of efficient causal models. The goal is to enhance our ability to use causal relations in developing algorithms for discovering causal networks.
