David Sontag and Uri Shalit
In many fields such as healthcare, education, and economics, policy makers have increasing amounts of data at their disposal. Making policy decisions based on this data often involves causal questions: Does medication X lead to lower blood sugar, compared with medication Y? Does longer maternity leave lead to better child social and cognitive skills? These questions have to be addressed in practice, every day, by scientists working across many different disciplines.
The goal of this tutorial is to bring machine learning practitioners closer to the vast field of causal inference as practiced by statisticians, epidemiologists and economists. We believe that machine learning has much to contribute in helping answer such questions, especially given the massive growth in the available data and its complexity. We also believe the machine learning community could and should be highly interested in engaging with such problems, considering the great impact they have on society in general.
We hope that participants in the tutorial will: a) learn the basic language of causal inference as exemplified by the two most dominant paradigms today: the potential outcomes framework, and causal graphs; b) understand the similarities and the differences between problems machine learning practitioners usually face and problems of causal inference; c) become familiar with the basic tools employed by practicing scientists performing causal inference, and d) be informed about the latest research efforts in bringing machine learning techniques to address problems of causal inference.