‘Correlation does not imply causation’ is a famous saying in statistics. The goal of many (scientific) studies is to draw conclusions on whether a so-called “treatment” has some effect or not, e.g. assigning a drug, or showing homepage A or homepage B to a visitor. As mentioned, it is in general not sufficient to observe statistical association of phenomena to infer causal relationships, but it is possible to make assumptions allowing for it. In this talk I want to define causal effects and what is necessary for causal inference.

A soft introduction to causal inference

# References

[Her19C]

Causal Inference: What if,