Covariance induced by a shared latent variable ($\mathrm{Cov}(A,B)=\mathrm{Var}(g(T))$)

Let $A,B$ be indicator random variables with conditional independence given a random variable $T$, and suppose $\mathbb{E}[A\mid T]=\mathbb{E}[B\mid T]=g(T)$. Then the law of total expectation gives $\mathbb{E}[AB]=\mathbb{E}[g(T{)}^2]$ and $\mathbb{E}[A]=\mathbb{E}[B]=\mathbb{E}[g(T)]$. Thus their covariance is the variance of $g(T)$ and is nonnegative. The result explains correlated component evolutionary states in a coeval binary population without correlation between initial masses.