FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
This work addresses the challenge of identifying causal relationships in linear non-Gaussian models with latent confounders, which is an incremental improvement for researchers in causal inference.
The authors tackled the problem of causal discovery with latent confounders by proposing FRITL, a hybrid method combining constraint-based and independent noise-based approaches, which they validated through extensive experiments on simulated and real-world data.
We consider the problem of estimating a particular type of linear non-Gaussian model. Without resorting to the overcomplete Independent Component Analysis (ICA), we show that under some mild assumptions, the model is uniquely identified by a hybrid method. Our method leverages the advantages of constraint-based methods and independent noise-based methods to handle both confounded and unconfounded situations. The first step of our method uses the FCI procedure, which allows confounders and is able to produce asymptotically correct results. The results, unfortunately, usually determine very few unconfounded direct causal relations, because whenever it is possible to have a confounder, it will indicate it. The second step of our procedure finds the unconfounded causal edges between observed variables among only those adjacent pairs informed by the FCI results. By making use of the so-called Triad condition, the third step is able to find confounders and their causal relations with other variables. Afterward, we apply ICA on a notably smaller set of graphs to identify remaining causal relationships if needed. Extensive experiments on simulated data and real-world data validate the correctness and effectiveness of the proposed method.