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An Improved Neural Network Model for Treatment Effect Estimation

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 647)

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.

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References

  1. Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201–213 (2002)

    Article  MathSciNet  Google Scholar 

  2. Dorie, V.: NPCI: non-parametrics for causal inference (2016). https://github.com/vdorie/npci

  3. Finner, H.: On a monotonicity problem in step-down multiple test procedures. J. Am. Statist. Assoc. 88(423), 920–923 (1993)

    Article  MathSciNet  Google Scholar 

  4. Glass, T.A., Goodman, S.N., Hernán, M.A., Samet, J.M.: Causal inference in public health. Annu. Rev. Publ. Health 34, 61–75 (2013)

    Article  Google Scholar 

  5. Gretton, A., Borgwardt, K.M., Rasch, M.J., Schölkopf, B., Smola, A.: A kernel two-sample test. J. Mach. Learn. Res. 13(1), 723–773 (2012)

    MathSciNet  MATH  Google Scholar 

  6. Gulli, A., Pal, S.: Deep Learning with Keras. Packt Publishing Ltd. (2017)

    Google Scholar 

  7. Gustafsson, J.E.: Causal inference in educational effectiveness research: a comparison of three methods to investigate effects of homework on student achievement. School Effectiv. School Improv. 24(3), 275–295 (2013)

    Article  Google Scholar 

  8. Hill, J.L.: Bayesian nonparametric modeling for causal inference. J. Comput. Graph. Statist. 20(1), 217–240 (2011)

    Article  MathSciNet  Google Scholar 

  9. Hodges, J., Lehmann, E.L.: Rank methods for combination of independent experiments in analysis of variance. In: Selected Works of EL Lehmann, pp. 403–418. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-1412-4_35

  10. Johansson, F., Shalit, U., Sontag, D.: Learning representations for counterfactual inference. In: International Conference on Machine Learning, pp. 3020–3029. PMLR (2016)

    Google Scholar 

  11. Van der Laan, M.J., Rose, S., et al.: Targeted Learning: Causal Inference for Observational and Experimental Data, vol. 4. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-9782-1

  12. Livieris, I.E., Kiriakidou, N., Kanavos, A., Vonitsanos, G., Tampakas, V.: Employing constrained neural networks for forecasting new product’s sales increase. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2019. IAICT, vol. 560, pp. 161–172. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19909-8_14

  13. Louizos, C., Shalit, U., Mooij, J.M., Sontag, D., Zemel, R., Welling, M.: Causal effect inference with deep latent-variable models. Adv. Neural Inf. Process. Syst. 30 (2017)

    Google Scholar 

  14. Pandit, S., Gupta, S., et al.: A comparative study on distance measuring approaches for clustering. Int. J. Res. Comput. Sci. 2(1), 29–31 (2011)

    Article  Google Scholar 

  15. Qian, N.: On the momentum term in gradient descent learning algorithms. Neural Netw. 12(1), 145–151 (1999)

    Article  Google Scholar 

  16. Rubin, D.B.: Causal inference using potential outcomes: Design, modeling, decisions. J. Am. Statist. Assoc. 100(469), 322–331 (2005)

    Article  MathSciNet  Google Scholar 

  17. Shalit, U., Johansson, F.D., Sontag, D.: Estimating individual treatment effect: generalization bounds and algorithms. In: International Conference on Machine Learning, pp. 3076–3085. PMLR (2017)

    Google Scholar 

  18. Shi, C., Blei, D.M., Veitch, V.: Adapting neural networks for the estimation of treatment effects. arXiv preprint arXiv:1906.02120 (2019)

  19. Singh, A., Yadav, A., Rana, A.: \(k\)-means with three different distance metrics. Int. J. Comput. Appl. 67(10) (2013)

    Google Scholar 

  20. Varian, H.R.: Causal inference in economics and marketing. Proc. Natl. Acad. Sci. 113(27), 7310–7315 (2016)

    Article  Google Scholar 

  21. Villani, C.: Optimal Transport: Old and New, vol. 338. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-71050-9

  22. Yoon, J., Jordon, J., Van Der Schaar, M.: GANITE: estimation of individualized treatment effects using generative adversarial nets. In: International Conference on Learning Representations (2018)

    Google Scholar 

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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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Correspondence to Niki Kiriakidou .

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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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  • Print ISBN: 978-3-031-08336-5

  • Online ISBN: 978-3-031-08337-2

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