Messerli (2012)
12th January, 2021
Dablander (2020)
Causal inference
- In all previous examples, we can achieve good to excellent predictions
- But we do not merely want to predict systems, but also change them!
- Causal inference goes beyond prediction by modeling the outcome of interventions
- Ask not what \(Y\) is likely to be if \(X\) happened to be \(x\)
- Ask what \(Y\) is likely to be if \(X\) were set to \(x\)
- It provides tools that allow us to draw causal conclusions from observational data
- Because randomized experiments are often infeasible, unethical, or impossible
Causal inference
- In all previous examples, we can achieve good to excellent predictions
- But we do not merely want to predict systems, but also change them!
- Causal inference goes beyond prediction and models the outcome of interventions
- It provides tools that allow us to draw causal conclusions from observational data
