Place and timeThe lecture will run online. Starting at 5 pm.
The Thinking module launches the Hard Core Philosophy project. We want to show you that philosophy today is not only about popular methods of critical thinking and great ideas of the past.
The Hard Core Philosophy project presents the results of modern philosophical research to the general public. Here, philosophy is understood not as free-thinking or a form of organizing cultural leisure. But the core of philosophical research is the search for methods and paradigms for the analysis of fundamental problems: from the problem of the relationship between consciousness and the brain to the threat of “digital slavery” or hope for a bright future for the posthuman. Now, philosophers, scientists, and engineers are allies, because ultimately everyone is concerned about one question: what future are we making today?
The project’s curator is Darya Chirva, head of ITMO’s Thinking module.
On October 2 at 5 pm the first event of the Hard Core Philosophy project will take place – a public lecture followed by a discussion in English. The topic is “Causal inference in evidence-based policy. A tale of three monsters and how to chase them away”.
We live in the age of the planned policy. Every step of any kind of governance is supposed to be supported by some kind of evidence. But what exactly is good evidence and how is it best used in order to infer the efficacy of such a policy? The paradigmatic example of good evidence is randomized control trials (RCTs). In this lecture, the threats that come with the orthodox view about how RCTs provide evidence for a planned policy are discussed. It is argued that an RCT that showed that the policy worked in a specific domain, though it points at the right causal connection between the policy and the intended outcome, is insufficient because it typically only reveals a small part of the more general causal structure. But way richer causal information is required for reliably predicting a policy’s efficacy in an intended domain. Classical inference patterns are discussed and it is explored how recent advances in AI (causal Bayesian networks) can supplement or even replace them.
The speakers are Dr. Alexander Gebharter and Dr. Christian Feldbacher-Escamilla.
The lecture will be held on Zoom. All registered users will receive a link to join the lecture via email. To register for the event, please head here.