Editing Causal Inference (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Causal inference is the quest for the '''Counterfactual'''.

'''The Fundamental Problem of Causal Inference''': You can never observe the same person in two different states at the same time. You either took the pill or you didn't. We can never ''know'' for sure what would have happened if you hadn't taken it. Therefore, we have to find clever ways to "simulate" the counterfactual.

'''Judea Pearl's Ladder of Causation''':
1. '''Association''' (Seeing): "If I see X, how likely is Y?" (Standard Machine Learning).
2. '''Intervention''' (Doing): "If I ''do'' X, what will happen to Y?" (Causal Inference).
3. '''Counterfactuals''' (Imagining): "If I had done X instead of Z, what ''would have'' happened?" (The highest form of human/AI reasoning).

'''The Back-Door Criterion''': If you want to know if X causes Y, you must "close the back door"—meaning you must control for all the variables that might be causing both X and Y. If you don't, your result will be "biased" by the '''Confounder'''.
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