Projects per year
Abstract
The abundance of data produced daily from large variety of sources has boosted the need of novel approaches on causal inference analysis from observational data. Observational data often contain noisy or missing entries. Moreover, causal inference studies may require unobserved high-level information which needs to be inferred from other observed attributes. In such cases, inaccuracies of the applied inference methods will result in noisy outputs. In this study, we propose a novel approach for causal inference when one or more key variables are noisy. Our method utilizes the knowledge about the uncertainty of the real values of key variables in order to reduce the bias induced by noisy measurements. We evaluate our approach in comparison with existing methods both on simulated and real scenarios and we demonstrate that our method reduces the bias and avoids false causal inference conclusions in most cases.
Original language | English |
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Title of host publication | 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 278-285 |
Number of pages | 8 |
Volume | 2017-May |
ISBN (Electronic) | 9781509061815 |
DOIs | |
Publication status | E-pub ahead of print - 3 Jul 2017 |
Event | 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States Duration: 14 May 2017 → 19 May 2017 |
Conference
Conference | 2017 International Joint Conference on Neural Networks, IJCNN 2017 |
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Country/Territory | United States |
City | Anchorage |
Period | 14/05/17 → 19/05/17 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence
Fingerprint
Dive into the research topics of 'Probabilistic matching: Causal inference under measurement errors'. Together they form a unique fingerprint.Projects
- 1 Finished
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Trajectories of Depression: Investigating the Correlation between Human Mobility Patterns and Mental Health Problems by means of Smartphones
Musolesi, M.
Engineering & Physical Science Research Council
31/03/14 → 30/05/15
Project: Research Councils