Ordering-Based Causal Structure Learning in the Presence of Latent Variables

Autor:	Bernstein, Daniel Irving, Saeed, Basil, Squires, Chandler, Uhler, Caroline
Rok vydání:	2019
Předmět:	Mathematics - Statistics Theory Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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 constraint-based, we here propose a score-based approach. We prove that under assumptions weaker than faithfulness, any sparsest independence map (IMAP) of the distribution belongs to the Markov equivalence class of the true model. This motivates the \emph{Sparsest Poset} formulation - that posets can be mapped to minimal IMAPs of the true model such that the sparsest of these IMAPs is Markov equivalent to the true model. Motivated by this result, we propose a greedy algorithm over the space of posets for causal structure discovery in the presence of latent confounders and compare its performance to the current state-of-the-art algorithms FCI and FCI+ on synthetic data. Comment: To appear in AISTATS 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.09014 Zobrazit plný text záznamu View this record from Arxiv