Say we have two random variables $X\sim B(p_1),\ Y\sim B(p_2)$ where $B(p)=$ Bernoulli with probability $0\le p\le 1$. I am interested in the case when the correlation $\rho$ of $X,Y$ tends to $1$.
If we set events $A=\{X=1\}$, $B=\{Y=1\}$, the conditional probability property gives us $$\begin{align}\tag{1} P(A\cap B) = P(A\mid B)\cdot P(B)\\ = P(B\mid A)\cdot P(A) \end{align}$$ When $\rho=1$ we must have $P(A\mid B)=P(B\mid A)=1$ (since one event implies the other). Equation $(1)$ then yields $$P(A) = P(B)$$ Furthermore, if correlation is high (= tending to 1), then whenever event $A$ occurs then $B$ must also occur (and vice versa). So the probabilities of $A,B$ should be $$P(A)=P(B)=\max(p_1,p_2)\tag{2}$$ However, for any $\rho=1-\epsilon$ with $\epsilon>0$, we still have $P(A)=p_1$ and $P(B)=p_2$ as per definition.
So can $(2)$ even be correct? What am I missing?
Unfortunately, Joint distribution of dependent Bernoulli Random variables only discusses non-deterministic sequences, so it doesn't quite apply.
