Dr. Lucas Kook of Vienna University of Economics and Business (WU Wien), Austria, who specialises in statistical methods like conditional independence testing for causal inference, machine learning, and various types of data structures, will give the talk entitled. 

Title: Probing Causal Models: Criticism, Falsification, and Sensitivity Analysis

Abstract: 

In statistics, the concept of causality has been formalized in numerous ways, including potential outcomes, causal graphical models, and structural causal models. What fundamentally distinguishes such causal models from purely statistical models is their ability to describe how a system behaves under interventions. In many settings, interventions are infeasible, forcing scientists to rely on purely observational data to draw causal conclusions. Doing so, however, requires strong and typically untestable assumptions. This makes it imperative to be able to critically assess both the causal model itself and any conclusions drawn from it. In this talk, I will first provide an introduction to graphical approaches to causal inference. I will then focus on methods for criticising, falsifying, and probing the sensitivity of causal models.

All welcome!

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