Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders
This addresses the challenge of causal inference in fields like epidemiology or social sciences where hidden variables can bias results, representing a novel method for a known bottleneck.
The paper tackles the problem of causal discovery from data with latent confounders by proposing a repetitive causal discovery (RCD) method, which identifies causal directions and latent confounders between observed variables, with experimental validation on simulated and real-world data confirming its effectiveness.
Causal discovery from data affected by latent confounders is an important and difficult challenge. Causal functional model-based approaches have not been used to present variables whose relationships are affected by latent confounders, while some constraint-based methods can present them. This paper proposes a causal functional model-based method called repetitive causal discovery (RCD) to discover the causal structure of observed variables affected by latent confounders. RCD repeats inferring the causal directions between a small number of observed variables and determines whether the relationships are affected by latent confounders. RCD finally produces a causal graph where a bi-directed arrow indicates the pair of variables that have the same latent confounders, and a directed arrow indicates the causal direction of a pair of variables that are not affected by the same latent confounder. The results of experimental validation using simulated data and real-world data confirmed that RCD is effective in identifying latent confounders and causal directions between observed variables.