Tsapeli, F;
Tino, P;
Musolesi, M;
(2017)
Probabilistic Matching: Causal Inference under Measurement Errors.
In:
Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2017.
(pp. pp. 278-285).
IEEE: Anchorage, AK, USA.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Probabilistic Matching: Causal Inference under Measurement Errors |
| Event: | International Joint Conference on Neural Networks (IJCNN) 2017 |
| Location: | Anchorage, AK |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/IJCNN.2017.7965866 |
| Publisher version: | https://doi.org/10.1109/IJCNN.2017.7965866 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Random variables, Noise measurement, Mathematical model, Probability distribution, Probabilistic logic, Social network services, Genetics,data handling, inference mechanisms, measurement errors, probability,measurement errors, causal inference analysis, observational data, noisy entries, missing entries, noisy measurements, probabilistic matching |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1553495 |
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