Bias of difference-in-means estimator for experiments randomized using Bernoulli trials

Under potential outcomes framework (Neyman-Rubin causal model), it is straighforward to show that difference-in-means is an unbiased estimator of average treatment effect under completely randomized trials (when we fix the number of experiment units in treatment/control groups).

However, in Bernoulli trials experiment, each unit is randomly assigned to treatment/control group with some fixed probability. The bias computation of difference-in-means estimator is more difficult because the number of units in control/treatment group is now a random variable.

Is the difference-in-means still unbiased for Bernoulli trials randomized experiments? Is the way to prove this using law of iterated expectation, by conditioning on the number of units in treatment group?

$$E[Y|A = a] =E[Y(a)|A=a]=E[Y(a)]$$ for each treatment vaue $$a$$, implying the difference in means estimator, which is unbiased for $$E[Y|A = 1] - E[Y|A = 0]$$, is unbiased for $$E[Y(1)] - E[Y(0)]$$.
The first equality is due to causal consistency, i.e., $$Y = AY(1) + (1-A)Y(0)$$, which implies that $$Y(a)$$ and $$Y$$ are equivalent when conditoning on $$A=a$$. The second equality comes from confoundedness, that $$A \perp Y(a)$$ for each $$a$$, which implies that $$f(Y(a)|A) = f(Y(a))$$. In a Bernoulli trial, unconfoundedness holds because assignment to treatment does not depend on any factors related to the potential outcomes (i.e., it is a fixed probability for each individual).