A Meta Learning Approach to Discerning Causal Graph Structure
This work addresses the challenge of causal discovery in complex scenarios for researchers in machine learning and statistics, though it appears incremental as it builds on existing meta-learning and causal inference methods.
The paper tackled the problem of inferring causal direction between variables in complex graphs with latent confounders by using a meta-learning approach to optimize distribution simplicity, resulting in a model that is robust to modest data scarcity but less so to distributional changes, and it demonstrated ability to infer causal relationships.
We explore the usage of meta-learning to derive the causal direction between variables by optimizing over a measure of distribution simplicity. We incorporate a stochastic graph representation which includes latent variables and allows for more generalizability and graph structure expression. Our model is able to learn causal direction indicators for complex graph structures despite effects of latent confounders. Further, we explore robustness of our method with respect to violations of our distributional assumptions and data scarcity. Our model is particularly robust to modest data scarcity, but is less robust to distributional changes. By interpreting the model predictions as stochastic events, we propose a simple ensemble method classifier to reduce the outcome variability as an average of biased events. This methodology demonstrates ability to infer the existence as well as the direction of a causal relationship between data distributions.