Causal inference is a branch of applied statistics which seeks to identify the causal connections between phenomena. It is a tool worth having in the toolset of a data analyst in addition to hypothesis testing and machine learning methods.

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).