Causal Inference where the treatment assignment is randomized

I have mostly worked with Observational data where the treatment assignment was not randomized. In the past, I have used PSM, IPTW to balance and then calculate ATE. My problem is: Now I am working on a problem where the treatment assignment is randomized meaning there won't be a confounding effect. But treatment and control groups have different sizes. There's a bucket imbalance.

Now should I just analyze the data as it is and run statistical significance and Statistical power test? Or shall I balance the imbalance of sizes between the treatment and control using let's say covariate matching and then run significance tests?