Meta-learning for heterogeneous treatment effect estimation with closed-form solvers

Author:

Iwata TomoharuORCID,Chikahara Yoichi

Publisher

Springer Science and Business Media LLC

Reference46 articles.

1. Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235–267.

2. Alaa, A. M., & Van Der Schaar, M. (2017). Bayesian inference of individualized treatment effects using multi-task Gaussian processes. Advances in Neural Information Processing Systems 30 (pp. 3427–3435).

3. Battocchi, K., Dillon, E., Hei, M., et al. (2019). EconML: A Python package for ML-based heterogeneous treatment effects estimation. https://github.com/microsoft/EconML, version 0.13.1.

4. Bengio, Y., Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A., & Pal, C. (2019). A meta-transfer objective for learning to disentangle causal mechanisms. In International conference on learning representations.

5. Bertinetto, L., Henriques, J. F., Torr, P. H., & Vedaldi, A. (2018). Meta-learning with differentiable closed-form solvers. In International conference on learning representations.

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