machine learning techniquesmaximum likelihoodpharmacoepidemiologysimulationBackground Doubly robust estimation produces an unbiased estimator for the average treatment effect unless both propensity score (PS) and outcome models are incorrectly specified. Studies have shown that the doubly robust estimator is ...
Doubly Robust Learning-一种去偏方法双重稳健估计-DRL是一种处理基于观测数据进行因果建模的方法。 大家已知的是,观测数据是有偏的,即存在特征X既影响目标outcome Y,又影响Treatment T。那么在进行因果建模之…
1.1 DR的理论基础 【因果推断/uplift建模】Doubly Robust Learning(DRL) Doubly Robust Methods明显优点是两个预估量如果有一个是consistent,则ATE是估计是consistent; 还有一个优点是理论上比COM/IPW收敛更快,也就是说理论上数据利用效率更高,但是理论研究一般是基于infinite data进行的,真实环境中收敛速率也不一定。
1 DR :Doubly Robust 1.1 DR的理论基础 1.1.1 ATE的估计 1.1.2 CATE的估计 1.2 DR 与DML的异同 2 econml的实现 这个系列文章: 因果推断笔记--python 倾向性匹配PSM实现示例(三)mattzheng.blog.csdn.net/article/details/119887208 悟乙己:因果推断笔记——DML :Double Machine Learning案例学习(十六)175...
doubly robust outcome weighted learning (DDROWL) that can handle big and complex data. This is a machine learning tool that directly estimates the optimal decision rule and achieves the best of three worlds: deep learning, double robustness, and residual weighted learning. Two architectures have ...
and Qi J. Doubly robust joint learning for recommendation on data missing not at random. In International Conference on Machine Learning (ICML), 2019概为了处理推荐系统中的 MNAR (Missing Not At Random) 情况, 作者结合插值模型和 inverse-propensity-scoring (IPS) estimator 构造了一宗 Doubly Robust ...
Our analysis shows that the proposed estimator is robust against nuisance model misspecification, and can attain fast n rates with tractable inference even when using nonparametric machine learning approaches. We study the empirical performance of our methods by simulation and apply them for recidivism ...
adr: Advantage Doubly Robust (ADR) Estimator for Learning When-to-Treat Policies This repository implements ADR for learning when-to-treat policies, as proposed by Nie, Brunskill and Wager (2019). Authors This package is written and maintained by Xinkun Nie (xinkun@stanford.edu). ...
论文阅读 | Robust Neural Machine Translation with Doubly Adversarial Inputs (1)用对抗性的源实例攻击翻译模型; (2)使用对抗性目标输入来保护翻译模型,提高其对对抗性源输入的鲁棒性。 生成对抗输入:基于梯度 (平均损失) ->AdvGen 我们的工作处理由白盒NMT模型联合生成的扰动样本 -> 知道受攻击模型的参数...
Recently, Machine Learning (ML) techniques are widely used to solve the regression and control problems [32], [33], [34], [35], [36]. Hybrid ML algorithms aim to approximate the nonlinear function and find the best results during the learning stage the small training errors. In [37], ...