Abstract
Nowadays, in many scientific and industrial fields there is an increasing need for estimating treatment effects and answering causal questions. The key for addressing these problems is the wealth of observational data and the processes for leveraging this data. In this work, we propose a new model for predicting the potential outcomes and the propensity score, which is based on a neural network architecture. The proposed model exploits the covariates as well as the outcomes of neighboring instances in training data. Numerical experiments illustrate that the proposed model reports better treatment effect estimation performance compared to state-of-the-art models.
Keywords
- Causal inference
- Dragonnet
- Treatment effect
- Potential outcomes
- Propensity score
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Acknowledgements
The work leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 965231, project REBECCA(REsearch on BrEast Cancer induced chronic conditions supported by Causal Analysis of multi-source data).
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Kiriakidou, N., Diou, C. (2022). An Improved Neural Network Model for Treatment Effect Estimation. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Cortez, P. (eds) Artificial Intelligence Applications and Innovations. AIAI 2022. IFIP Advances in Information and Communication Technology, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-08337-2_13
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DOI: https://doi.org/10.1007/978-3-031-08337-2_13
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