The document provides an introduction to causal inference, highlighting the limitations of traditional regression models in determining cause and effect without contextual understanding. It discusses different types of variables and emphasizes that correlation does not imply causation, using the example of violent crime and cucumber consumption in Russia. Additionally, it recommends several books and R packages for further study in causal inference.

1. Causal Inference:
Short Introduction
Serhii Kushchenko, Bratislava, 16.09.2019
https://www.linkedin.com/in/kushchenko/

2. Problem with Traditional Regression
Y = k * X + b ⇨
⇨ X = 1/k * Y - b/k
Which variable is a cause
and which is an effect here?
We can not tell without
knowing the context.

3. Types of Variables
1. Continuous - blood glucose level - 0-30 mmol/l.
2. Categorical (factor) - patient’s country of origin -
countable number of levels.
3. Continuous artificially made categorical, i.e. numerical
range divided into subgroups. Blood glucose level “low”,
“normal”, “high”.

4. Correlation ≠ Causation
Categorical variable
or continuous variable,
artificially made
categorical

5. In Russia, violent crime is strongly correlated
with salted cucumbers consummation. Why?
≈

6. Things Are Not Always as They Seem First

7. Things Are Not Always as They Seem First
Color allows
you to display
information
about the third
categorical
variable

8. Causal Inference: Structures - 1

9. Causal Inference: Structures - 2

10. Causal Inference: Structures - 3

12. Causal Inference Ladder

13. Differences Between Traditional Statistics and CI

14. Recommended Books
1. The Book of Why by Judea Pearl and Dana Mackenzie
2. Causal Inference in Statistics: A Primer by Judea Pearl
and others + Solution Manual
3. Mostly Harmless Econometrics by Joshua D. Angrist
and Jörn-Steffen Pischke
4. R packages: dagitty (structural causal models)
and lavaan (structural equation modeling).