Causal Discovery from Incomplete Data: A Deep Learning Approach
This addresses the challenge of missing data in causal discovery for autonomous systems, though it appears incremental as it builds on existing methods by adding imputation.
The paper tackles the problem of causal discovery from incomplete data by proposing a deep learning framework called Imputated Causal Learning (ICL), which iteratively imputes missing data and discovers causal structures, showing it outperforms state-of-the-art methods in simulations.
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.