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We develop new algorithms for estimating heterogeneous treatment effects, combining recent developments in transfer learning for neural networks with insights from the causal inference literature.
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A simple method for estimating interactions between a treatment and a large number of covariates
Tian, L., Alizadeh, A. A., Gentles, A. J., and Tibshirani, R. (2014) · 2014
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Machine learning methods for estimating heterogeneous causal effects
Athey, S. and Imbens, G. W. (2015) · 2015
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Recursive partitioning for heterogeneous causal effects
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Meta networks
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Learning objectives for treatment effect estimation
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Estimation and inference of heterogeneous treatment effects using random forests
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On first-order meta-learning algorithms
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Powers, S., Qian, J., Jung, K., Schuler, A., Shah, N. H., Hastie, T., and Tibshirani, R. (2018) · 2018
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