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Adapting Neural Networks for the Estimation of Treatment Effects We propose two adaptations based on insights from the statistical literature on the estimation of treatment effects The first is a new architecture, the Dragonnet, that exploits the sufficiency of the propensity score for estimation adjustment
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8. Dragon Net — Causal Decision Making Therefore, Dragonnet [8] aims to answer this question: how can we adapt the design and training of the neural networks used in the first step in order to improve the quality of treatment effect estimation?
arXiv:1906. 02120v2 [stat. ML] 17 Oct 2019 on the estimation of treatment effects The first is a new architecture, the Dragonnet, that exploits the sufficiency of the pr pensity score for estimation adjustment The second is a regularization procedure, targeted regularization, that induces a bias towards models that have non-parametrically optimal as
causalml causalml inference tf dragonnet. py at master - GitHub The authors propose two adaptations: - A new architecture, the Dragonnet, that exploits the sufficiency of the propensity score for estimation adjustment - A regularization procedure, targeted regularization, that induces a bias towards models that have non-parametrically optimal asymptotic properties ‘out-of-the-box’
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