- [[causal revolution]], [[causal inference]] # Idea [[Judea Pearl]] distinguished three levels of causal inference: - **association**: $P(y|x)$ - seeing: what is? - the probability of $Y = y$ given that we observe $X = x$ - **intervention**: $P(y|do(x))$ - manipulation/experimentation/doing: what if? - the probability of $Y = y$ given that we intervene and set the value of $X$ to $x$ - **counterfactuals**: $P(y_x|x', y')$ - imagining/retrospecting: what if I had acted differently? - the probability of $Y = y$ if $X$ had been $x$ given that we actually observed $x'$, $y'$ These three *levels* related to data science's three tasks: description, prediction, [[causal inference|causal inference]]. # References