In machine learning, there has been great progress in obtaining powerful predictive models, but these models rely on correlations between variables and do not allow for an understanding of the underlying mechanisms or how to intervene on the system for achieve a certain goal. The concepts of causality are fundamental to have levers for action, to formulate recommendations and to answer the following questions: "what would happen if » we had acted differently?
The questions of causal inference arise in many areas (socio-economics, politics, psychology, medicine, etc.): depending on the context which drug to use to improve the patient's health? what marketing strategy for product placement should be used to influence consumer buying behavior, etc. The formalism of causal inference makes it possible to study these questions as a problem of classical statistical inference. The gold standard for estimating the effect of treatment is a randomized controlled trial (RCT) which is, for example, mandatory for the authorization of new drugs in pharmaceutical and medical research. However, RCTs are generally very expensive in terms of time and financial costs, and in some areas such as economics or political science, it is often not possible to implement an RCT, for example to assess the effectiveness of a given policy.
The aim of this course is to present the available methods to perform causal inference from observational data. We focus on both the theoritical framework and practical aspects (available software solution).
In terms of application, the lecture will be illustrated with recent exemples mainly in the field of health : What is the effect of Hydrochloroquine on survival ? What would have happened if Italy’s government had waited a week before imposing lockdown measures ? Ect.
Numerus Clausus : 40
Instructor: Julie Josse firstname.lastname@example.org