Causality: from data to science

Relationships between causes and their effects take a central role in various scientific and societal questions. Does the new COVID-19 vaccine protect better against hospitalization than the old version? How doese a cancer cell react to exposure to a certain chemical? What genetic properties cause a plant to be more resistant against drought? What will inflation in the Netherlands go down if the European Central Bank increases the interest rate? These are all examples of causal questions, which are about predicting the consequences of certain actions as accurately as possible. Traditionally, scientists try to answer causal questions by means of a combination of data and mathematical models. Given the recent impressive successes of deep learning, and the exploding availability of data, one might be led to believe that modelling and even conducting experiments is becoming redundant (as long as sufficient data is available). In my inaugural lecture, I explain why causal models as well as experimental research will nevertheless remain necessary to provide reliable answers to causal questions.