Data don’t speak for themselves: Correct conclusions can only be drawn against the background of its history, the so-called data-generating process. With the help of the elements of causal inference, we can explain seemingly paradoxical patterns and biases in our data. In this introductory course, the basics of causal inference are explained with the help of causal diagrams.

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• Potential outcomes and counterfactuals
• Causal effects and sources of bias
• Directed Acyclic Graphs as a means of describing the data-generating process
• Different levels of data application and their requirements

After completing the course I will be able to...

• state the premises for causal inferences
• describe the basic elements of graphical causal modeling
• identify sources of bias in simple examples 
• develop examples for different levels of data applications
• think carefully about correlation and causation

No requirements