Work

Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method

Year: 2020

Type: preprint

Abstract: We address the problem of distinguishing cause from effect in bivariate setting. Based on recent developments in nonlinear independent component analysis (ICA), we train nonparametrically general nonlinear causal models that allow non-additive noise. Further, we build an ensemble framework, namely Causal Mosaic, which models a causal pair by a mixture of nonlinear models. We compare this method with other recent methods on artificial and real world benchmark datasets, and our method shows state-of-the-art performance.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2001.01894

Authors: Wu, Pengzhou, Fukumizu, Kenji

Institutions: The Graduate University for Advanced Studies, SOKENDAI, The Institute of Statistical Mathematics

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3000279656

Cites: 25

Cited by: 10

Related to: 10