Estimating Counterfactual Outcomes of Time-varying Treatments using Deep Gaussian Process

Author:

Norimatsu Yoshiyuki1

Affiliation:

1. Information Technology R&D Center, Mitsubishi Electric Corporation

Publisher

Japanese Society for Artificial Intelligence

Subject

Artificial Intelligence,Software

Reference34 articles.

1. [Alaa 17a] Alaa, A. M. and van der Schaar, M.: Bayesian inference of individualized treatment effects using multi-task gaussian processes,Advances in Neural Information Processing Systems (NeurIPS), pp.3424–3432 (2017)

2. [Alaa 17b] Alaa, A. M. and van der Schaar, M.: Deep multi-task gaussian processes for survival analysis with competing risks, Advances in Neural Information Processing Systems (NeurIPS), pp. 2326-2334 (2017)

3. [Alvarez 12] Alvarez, M. A., Rosasco, L., Lawrence, N. D., et al.: Kernels for vector-valued functions: A review, Foundations and Trends in Machine Learning, Vol. 4, No. 3, pp. 195–266 (2012)

4. [Almond 05] D. Almond, K. Y. Chay, and D. S. Lee.: The costs of low birth weight, The Quarterly Journal of Economics, Vol. 120, No. 3, pp. 1031–1083 (2005)

5. [Bahdanau 15] Bahdanau, D., Cho, K., and Bengio, Y.: Neural machine translation by jointly learning to align and translate, International Conference on Learning Representations (ICLR) (2015)

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