Causal discovery of linear acyclic models with arbitrary distributions
This work addresses limitations in causal inference for continuous-valued data, offering a more robust method for researchers in statistics and machine learning, though it appears incremental by building on prior techniques.
The paper tackles the problem of causal discovery for linear acyclic models with arbitrary distributions, generalizing and combining existing approaches to learn model structure more accurately in cases where previous methods fail, as demonstrated through simulations.
An important task in data analysis is the discovery of causal relationships between observed variables. For continuous-valued data, linear acyclic causal models are commonly used to model the data-generating process, and the inference of such models is a well-studied problem. However, existing methods have significant limitations. Methods based on conditional independencies (Spirtes et al. 1993; Pearl 2000) cannot distinguish between independence-equivalent models, whereas approaches purely based on Independent Component Analysis (Shimizu et al. 2006) are inapplicable to data which is partially Gaussian. In this paper, we generalize and combine the two approaches, to yield a method able to learn the model structure in many cases for which the previous methods provide answers that are either incorrect or are not as informative as possible. We give exact graphical conditions for when two distinct models represent the same family of distributions, and empirically demonstrate the power of our method through thorough simulations.