摘要
由新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-详细的机器学习数据已经呈现。根据News Rx编辑在华盛顿州西雅图的新闻报道，该研究指出，“我们提出了一种反事实方法来训练‘因果意识’预测模型，这些模型能够在静态反干扰机器学习任务（即，输出影响输入的预测任务）中利用因果信息。”"appro ach可以用来产生不受混杂因素影响的预测."我们的新闻记者从Sage Bionetworks的研究中获得了一句话：“在涉及观察到的中介物的应用中，该方法可以用来概括只捕捉直接或间接因果影响的TE预测。”从机制上讲，我们在（反事实）模拟INP UTs上训练受监督的学习者，这些INP UTs只保留INP之间因果关系产生的关联。我们关注线性模型，其中连接协方差、因果效应和预测均方误差的分析结果很容易获得。相当重要的是，我们表明我们的方法不需要了解完整的CA图。知道哪些变量代表潜在的混杂因素和/或中介因素就足够了。我们研究了该方法对于选择偏差产生的DAT ASET偏移的稳定性，并通过将该方法扩展到能够更好地解释数据非线性的加性模型来放宽线性假设。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting out of Seattle, Washington, by News Rx editors, the research stated, "We propose a counterfactual approach to train ‘causality-aware' predictive models that are able to leverage causal information in static anticausal machine learning tasks (i.e., prediction tasks where the o utcome influences the inputs). In applications plagued by confounding, the appro ach can be used to generate predictions that are free from the influence of obse rved confounders." Our news journalists obtained a quote from the research from Sage Bionetworks, " In applications involving observed mediators, the approach can be used to genera te predictions that only capture the direct or the indirect causal influences. M echanistically, we train supervised learners on (counterfactually) simulated inp uts that retain only the associations generated by the causal relations of inter est. We focus on linear models, where analytical results connecting covariances, causal effects, and prediction mean square errors are readily available. Quite importantly, we show that our approach does not require knowledge of the full ca usal graph. It suffices to know which variables represent potential confounders and/or mediators. We investigate the stability of the method with respect to dat aset shifts generated by selection biases and also relax the linearity assumptio n by extending the approach to additive models better able to account for nonlin earities in the data."
NETL
NSTL