Abstract
Real-world datasets often contain many missing values due to several reasons. This is usually an issue since many learning algorithms require complete datasets. In certain cases, there are constraints in the real world problem that create difficulties in continuously observing all data. In this paper, we investigate if graphical causal models can be used to impute missing values and derive additional information on the uncertainty of the imputed values. Our goal is to use the information from a complete dataset in the form of graphical causal models to impute missing values in an incomplete dataset. This assumes that the datasets have the same data generating process. Furthermore, we calculate the probability of each missing data value belonging to a specified percentile. We present a preliminary study on the proposed method using synthetic data, where we can control the causal relations and missing values.
Original language | English |
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Title of host publication | Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings |
Editors | Marie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager |
Publisher | Springer |
Pages | 485-496 |
Number of pages | 12 |
Volume | 1237 CCIS |
ISBN (Print) | 9783030501457 |
DOIs | |
Publication status | Published - 1 Jan 2020 |
Event | 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems - Lisbon, Portugal Duration: 15 Jun 2020 → 19 Jun 2020 Conference number: 18 |
Publication series
Series | Communications in Computer and Information Science |
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Volume | 1237 CCIS |
ISSN | 1865-0929 |
Conference
Conference | 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems |
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Abbreviated title | IPMU 2020 |
Country/Territory | Portugal |
City | Lisbon |
Period | 15/06/20 → 19/06/20 |
Keywords
- Graphical causal models
- Missing data
- Uncertainty in missing values