【推荐】Counterfactual Machine Learning

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摘要

 

转自：洪亮劼

在这次KDD 2017上，有一个趋势就是越来越多的学者开始关注Causal Inference和Machine Learning的结合。其实有这样的趋势也是很自然的。工业界大家最早用Supervised Learning的方法来训练和评估模型。后来发现了线上系统和线下训练评估环境的差距。然后大家开始研究如何对产生的这种偏差定性、定量研究。Causal Inference为这种思潮提供了不少现成的工具。KDD 2017年的时间检验奖（Test of Time）得主Thorsten Joachims自己不仅在研究领域开启结合Causal Inference和Machine Learning这一新的方向，而且他还在康奈尔大学于这个春天开设了一门有关的课程叫Counterfactual Machine Learning。整个课程非常值得大家学习。可以说，Thorsten把目前的趋势总结成为了Bias in Human Feedback、Online Learning with Interactive Control、Batch Learning from Controlled Interventions以及Batch Learning from Observational Feedback，这应该算是有开创性的分类方法。相信会有更多的研究会在这几个方向跟进。

Course Description How many clicks will a new ad-placement system get? Will a different news-ranking algorithm increase the dwell times of the users? What ranking function will minimize abandonment in my search engine? Answering such evaluation and learning questions is at the core of improving many of the online systems we use every day. This seminar addresses the problem of using past human-interaction data (e.g. click logs) to learn to improve the performance of the system. This requires integrating causal inference models into the design of the learning algorithm, since we need to make predictions about the system's performance after an intervention (e.g. fielding a new ranking function).

链接：

http://www.cs.cornell.edu/courses/cs7792/2016fa/

原文链接：

https://m.weibo.cn/3193816967/4146423228075680

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