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前,我们需要进行去偏处理,使得Treatment Y独立于特征X,此时的观测数据近似相当于RCT数据,之后我们就可以使用...
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。
Public subsidies and innovation: a doubly robust machine learning approach leveraging deep neural networksDouble machine learningPublic subsidiesInnovationNonparametric estimationDeep neural networksEconomic growth is crucial to improve standards of living, prosperity, and welfare. R &D and knowledge spillovers...
在讲述Doubly Robust之前,我们还是要回到因果推断领域,回到我们需要解什么样的问题上来。大部分出现Doubly Robust的研究基本都会出现在CATE (Conditional Average Treatment Effect)的场景,一般情况下我们可以表示为(如果你对这个公式还不是很熟悉,说明你还是一个刚入坑因果推断的同学,后面的内容看起来不是那么顺畅其实也是...
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 ...
Public subsidies and innovation: a doubly robust machine learning approach leveraging deep neural networks Economic growth is crucial to improve standards of living, prosperity, and welfare. R &D and knowledge spillovers can offset the diminishing returns to phy... K Varaku,R Sickles,RM Kunst,....
In this work, we extend the doubly robust estimator for bandits to sequential decision-making problems, which gets the best of both worlds: it is guaranteed to be unbiased and can have a much lower variance than the popular importance sampling estimators. We demonstrate the estima...
We obtain designs which are optimally robust against possibly misspecified regression models, assuming that the parameters are to be estimated by one of se... DP Wiens,Wu, Eden K.H. - 《Computational Statistics & Data Analysis》 被引量: 42发表: 2010年 Robust prediction and extrapolation designs...
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...