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Causal Inference with Causal Graphical Models
Prof. Dr. Roland Müller
Causal thinking is the basis for prediction (X will lead to Y), explanation (Y was caused by X), and improvement (changing X will improve Y). Thus, causality is central to any scientific endeavor. But practitioners also use causal thinking because they must decide how to bring about a desirable event or prevent an undesirable one. Causal reasoning is, therefore, also critical in many controversial societal discussions, such as about COVID-19 or gender discrimination. But how can we think causally, and how can we draw causal inferences from experimental or observational data?
In this seminar, you will learn the essential elements of causal inference for observational and experimental studies. The target audience for this seminar spans from PhD students and professors who want to decide on the empirical and theoretical research design to decision-makers who want to use data to build their causal mental models. This is an introductory course in causality, so no prior knowledge is needed, except basic statistics. You can follow the course without prior programming knowledge, even though we will show some hands-on examples in Python.
Zoom Link:
https://us02web.zoom.us/j/84418077458?pwd=aUREeFh4aHJOY3d4N014cmY2ZXZZZz09
Meeting ID: 844 1807 7458
Passcode: 7CHmzB
