Key take aways
Without a casual model, data science is purely an exercise in data reduction. Statistics can only prove correlation but not causation. The human brain is a natural causal machine and is thus still relevant. Humans should go about identifying causality within our environment and delegate the ongoing monitoring to machines.
Components for consideration
Confounders
B and C are positively correlated but there are no direct causal links between the both of them. Without knowledge that A exists, B and C can be considered to be confounders.
- A therefore B
- A therefore C
- C and B both occur together
- B does not cause C
- C does not cause B
Colliders
In the example A and C are considered colliders because both of them will result in B
- A therefore B
- C therefore B
Figuring out the coefficients
- A therefore B
- B therefore C
- D therefore C
Front door techniques
Figuring out the cause model via path of A -> B -> C. This approach is only possible when you are able measure A, B and C
Backdoor techniques
Figuring out C by isolating B and D. This approach is used when not the entire causal chain is measurable.