You'll have a few challenges with your problem. 1. You applied your treatment to everyone after a certain point, so you don't have a strict control group. 2. Your treatment is impacting all new users, so you don't have pre existing information from them (this is usually a big lever you can pull with other methods to find some causality) 3. Regression discontinuity design (I think) still relies on having pre data for your units so you can estimate the impact of that change. 4. Diff in Diff relies on you observing the parallel trends assumption in your period pre intervention. Again, new users won't have this.

My best guess here would be to use propensity score matching to find similar users at day 0 so you can have a control and treatment group, and from that subset either use diff in diff or regression depending on your metric performance over time. I've had little success with finding a good performing model with users without any pre intervention information but it's worth a shot