I think you will struggle for the following reason; you are essentially trying to create a statistical test along the lines of:
$H_0$: impact (mean performance) on impacted mutual funds equals the mean performance on control group, i.e. ETFs, vs,
$H_1$: impact (mean performance) on impacted mutual funds is less than (or different to) the mean performance on control group, i.e. ETFs.
The problem is the parameter assumptions and your sample data;
- The assumption of equivalence between ETFs as control and mutual funds in the first place is open to criticism, and potentially of a larger variance than your underlying parameter of interest.
- You have a latent variable which is the proportion of mutual funds in your sample data that do not conform to the impacted group, meaning deriving the critical values (p-values) is unknown, even if you can isolate a test.
I believe you know the answer - the unimpacted mutual funds are the best control group and the impacted mutual funds provide the comparative data, which directly solves the above two problems.
Your question boils down to making estimates due to Thomson Reuters limitations whether or not you can still derive statistically significant results. In my opinion, I'm afraid not, especially since the very nature of the research suggests there may or may not be an effect so it is hard to observe. Sorry...