Explore/Exploit = Correlation/Causation!
Our paper “Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising” has appeared in JMLR. This paper takes the example of ad placement to illustrate how one can leverage causal inference to understand the behavior of complex learning systems interacting with their environment 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. In particular, the paper demonstrates the connection between the classic explore–exploit and correlation–causation issues in machine learning and statistics.