I like your proposal in theory but I doubt it works in practice. I'll try to explain: in practice what First Round has done is measuring post investment performance. I'm pretty sure that if the sample is big enough you will have an "all male" startup and a "with female" startup that failed thus the min being zero in both case. This doesn't mean there's no bias.

The point is that when you decide which company you finance (the source of bias) you make an estimation of future potential. But your test (and PG's one) measure ex-post results. Since there's a lot of uncertainty between the ex-ante and the ex-post measure, your test doesn't work.

Let me put in another way. The measure you are able to perform is not H(x-C)f(x) but a * H(x-C)f(x) + (1-a) * random where random is a random number and a is the weight of f(x) on the final outcome. You are right if a is 1, you and pg are wrong if a is 0 (but it will be a problem for the VCs), for everything in between you have to make assumption on the distribution of random.