Everyone has caught themselves playing the mind game of what would happen in the future if we changed our behaviour, or what would have happened in the past if we had done something differently. These conclusions are also known as causal inference and can be researched scientifically and mathematically. With his model, the Rubin Causal Model, Don Rubin has made one of the most significant contributions to this area of statistical research. On 18 January at 15:30, he will give a lecture on "Essential concepts of causal inference: a remarkable history and an intriguing future" in the Sky Lounge of the Faculty of Business and Economics (Oskar-Morgenstern-Platz). Influenced in his scientific thinking by Sir Ronald Fisher and Jerzy Neyman, Don Rubin compares the findings of these two with Heisenberg's uncertainty principle: At a single point in time, both the position and the momentum of a particle exist, and we can measure both, but we cannot measure both the momentum and the position of a particle at the same point in time. Causal inference is similar and both possible outcomes exist for an unit, where we can observe one of them at a given time, but not both together. This is also the fundamental dilemma within causal inference. With well over 250.000 citations according to Google Scholar Don Rubin is one the most highly cited scientific authors in the world. Also, as of the end of 2019, he has ten singly authored publications, each with over a thousand citations. He is also member of the National Academy of Sciences.
Please register for the talk: https://wiwi.univie.ac.at/donrubin/