Practically, a specific distribution $P_{Π = π} ( X , Y )$ is only known through a finite sample S_{Π = π} ( X , Y ) = {( x_1 , y_1 ), ( x_2 , y_2 ), cdots , ( x_n , y_n )} (a scatter plot). Hence, we can define a double random process for dataset generation: 1. Draw a pair $(P_{Π = π} ( X , Y ), G=g)$ from the mother distribution $P_M(P_{Π = π} ( X , Y ),G)$, where $G=g$ labels the causal relationship. 2. Draw $n$ samples from $P_{Π = π} ( X , Y )$ to form $S_{Π = π} ( X , Y )$. 3. Repeat the process to obtain enough training and/or test examples: { (S_1(X,Y),g_1), (S_2(X,Y),g_2), cdots, (S_N(X,Y),g_N) }.