ParceLiNGAM: A causal ordering method robust against latent confounders
We consider learning a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But, the estimation results could be distorted if some assumptions actually are violated. In this paper, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders. The key idea is to detect latent confounders by testing independence between estimated external influences and find subsets (parcels) that include variables that are not affected by latent confounders. We demonstrate the effectiveness of our method using artificial data and simulated brain imaging data.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Ordering-Based Causal Structure Learning in the Presence of Latent Variables
We consider the task of learning a causal graph in the presence of latent confounders given i.i.d.~samples from the model. While current algorithms for causal structure discovery in the presence of latent confounders are…
From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
We present a constraint-based algorithm for learning causal structures from observational time-series data, in the presence of latent confounders. We assume a discrete-time, stationary structural vector autoregressive pr…
Causal DiscoveryTime SeriesCausal discovery of linear non-Gaussian acyclic models in the presence of latent confounders
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 late…
Causal DiscoveryRelational Causal Discovery with Latent Confounders
Estimating causal effects from real-world relational data can be challenging when the underlying causal model and potential confounders are unknown. While several causal discovery algorithms exist for learning causal mod…
Causal InferenceMDL Meets Latent Confounders: LNML-based Causal Discovery
Causal discovery with nonlinear mechanisms and latent confounders remains challenging. Existing methods often rely on either linear assumptions or causal sufficiency, limiting their applicability. We propose an MDL-based…