Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
This work addresses causal inference challenges for researchers in fields like statistics and machine learning, but it is incremental as it builds on existing assumptions like faithfulness and non-Gaussianity.
The paper tackles the problem of learning causal models from observational data with latent variables in linear non-Gaussian systems, proposing methods to infer causal paths, orders, and sets of possible causal effects, with experiments demonstrating effectiveness on synthetic and real-world data.
We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effect of latent variables, one usually infers wrong causal relationships among the observed variables. Under faithfulness assumption, we propose a method to check whether there exists a causal path between any two observed variables. From this information, we can obtain the causal order among them. The next question is then whether or not the causal effects can be uniquely identified as well. It can be shown that causal effects among observed variables cannot be identified uniquely even under the assumptions of faithfulness and non-Gaussianity of exogenous noises. However, we will propose an efficient method to identify the set of all possible causal effects that are compatible with the observational data. Furthermore, we present some structural conditions on the causal graph under which we can learn causal effects among observed variables uniquely. We also provide necessary and sufficient graphical conditions for unique identification of the number of variables in the system. Experiments on synthetic data and real-world data show the effectiveness of our proposed algorithm on learning causal models.