Causal Reasoning and Learning Systems
published: Nov. 7, 2013, recorded: September 2013, views: 2455
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In complex real world systems, machine learning is used to influence actions, rather than just provide predictions. Those actions in turn influence the environment of the system. The goal of machine learning in these systems is therefore causal rather than correlational. e.g. what would be the survival chance of patient A if we gave them drug B (causal question); what is the survival chance of the patient A knowing that they were given drug B (correlational question). By injecting noise into actions taken by the system, we can collect data that allows us to infer causality, and predict the consequences of changes to the system. Such predictions allow both humans and algorithms to select changes that improve both the short-term and long-term performance of such systems. This work provides a framework (which can be viewed as a generalization of the A/B testing framework) for counterfactual causal inference in complex systems. Parts of this framework were implemented in Microsoft’s bing search advertising system, and I will show data from this implementation. This is work by Léon Bottou, which I had the good fortune of participating in.
Download slides: lsoldm2013_portugaly_learning_systems_01.pdf (2.2 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !