Causal Discovery from Incomplete Data: A Deep Learning Approach

• URL: http://arxiv.org/abs/2001.05343v1
• Date: Wed, 15 Jan 2020 14:28:21 GMT
• Title: Causal Discovery from Incomplete Data: A Deep Learning Approach
• Authors: Yuhao Wang, Vlado Menkovski, Hao Wang, Xin Du, Mykola Pechenizkiy
• Abstract summary: Imputated Causal Learning is proposed to perform iterative missing data imputation and causal structure discovery. We show that ICL can outperform state-of-the-art methods under different missing data mechanisms.
• Score: 21.289342482087267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As systems are getting more autonomous with the development of artificial intelligence, it is important to discover the causal knowledge from observational sensory inputs. By encoding a series of cause-effect relations between events, causal networks can facilitate the prediction of effects from a given action and analyze their underlying data generation mechanism. However, missing data are ubiquitous in practical scenarios. Directly performing existing casual discovery algorithms on partially observed data may lead to the incorrect inference. To alleviate this issue, we proposed a deep learning framework, dubbed Imputated Causal Learning (ICL), to perform iterative missing data imputation and causal structure discovery. Through extensive simulations on both synthetic and real data, we show that ICL can outperform state-of-the-art methods under different missing data mechanisms.

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