Four Lectures on Causality
Article Source: SDSC
Original Author: P. Rigollet
Published on: May 25, 2017
On May 10 and 11, Jonas Peters (University of Copenhagen) gave four lectures on causality at the Statistics and Data Science Center. These lectures were recorded and are available on the MIA youtube playlist (see also link below)
The slides for these lectures can be downloaded here.
In the field of causality we want to understand how a system reacts under interventions (e.g. in gene knock-out experiments). These questions go beyond statistical dependences and can therefore not be answered by standard regression or classification techniques. In this tutorial you will learn about the interesting problem of causal inference and recent developments in the field. No prior knowledge about causality is required.
Part 1: We introduce structural causal models and formalize interventional distributions. We define causal effects and show how to compute them if the causal structure is known.
Part 2: We present three ideas that can be used to infer causal structure from data: (1) finding (conditional) independencies in the data, (2) restricting structural equation models and (3) exploiting the fact that causal models remain invariant in different environments.
Part 3: If time allows, we show how causal concepts could be used in more classical machine learning problems.