Causal Inference in Time Series

What it is about

You will learn methods of causal inference for time series and apply them. By the end you will be able to identify causal structures in dynamic networks, apply suitable algorithms and implement results practically on real data sets.

What you will be able to do

• Understanding of causal models and causal reasoning in dynamic systems
• Recognize when problems can be modeled as networks
• Analysis of data with various network-analytic techniques
• Apply different procedures for causal structure discovery
• Derive courses of action based on interpretation of network analyses
• Continued work on complex problems and finding optimal solutions
• Critical thinking (causal reasoning)
• Research and evaluate sources

What the module consists of

• VorlesungDelivery of the theoretical foundations of causal inference and time series
• Übung/Praktische AufgabenApplication of theory to practical problems and discussion of solutions
• ProjektAnalysis of a real data set and implementation of software code to apply the learned methods

Teaching method

• VorlesungPresentation and explanation of the theoretical background
• DiskussionenDeepening and critical engagement with the content
• Übungen mit ÜbungsblätternIndependent deepening and application of the material; solutions are discussed