What is the best approach to evaluate the effect of an intervention on different segments? I have a dataset post A/B test which looks like this:
Group     | Segment         | Purchase ($)
Treatment | Small Business  | 10
Treatment | Medium Business | 15
Control   | Small Business  | 8
Control   | Medium Business | 8...

Now, I want to evaluate the impact of the treatment on Purchase ($) and if it was different by segment.
I can think of 4 options but I am not sure what to select:

*

*Run t-test for each segment separately. If so, what are the caveats?

*Run heterogeneous treatment estimation (HTE). If so, what to be aware of?

*Run Causal Forest. If so, what to be aware of?

*Uplift Model (regression model). If so, what to consider?

I would appreciate any tips.
Thanks!

Assuming that the treatment was assigned randomly, after coding Small Business as 0 and Medium Business as 1, the OLS coefficient $\beta_1$ will give you an unbiased estimate of the average causal effect of treatment on purchases for Small Businesses, while $\beta_1+\beta_3$ that for Medium Businesses.
$$purchases = \beta_0 + \beta_1 treatment +\beta_2 SegmentSize + \beta_3 treatment *SegmentSize  + \epsilon$$
I find the menu of your exotic options to be a curious one, especially since it does not seem to include the good old linear regression model. You'll get a lot more value out of mastering one statistical model such as linear regression than from superficially acquainting yourself with a whole host of exotic models.
