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Workshop 1 - Prof. Felix Thoemmes

"Identification and Estimation of Causal Effects"


How do we determine whether an observed effect can be interpreted causally? What are some possible threats to causal inference? 
In this workshop you will learn how to identify common threats to causal inference - e.g., confounding, selection - and also how address them. We will cover common estimation methods - e.g., regression, matching, weighting - and will practice them during the workshop in R.

Empfohlene Literatur
Rohrer, J. (2018). Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data. Advances in Methods and Practices in Psychological Science, 1, 27-42. 

Thoemmes, F., & Ong, A. D. (2016). A primer on inverse probability of treatment weighting and marginal structural models. Emerging Adulthood, 4, 40-59.

Here are two links to full books on many of the topics I will discuss:

R / RStudio


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