Hello all!

I’m new to Bayesian Modeling and have just completed the Intuitive Bayes course. I have started integrating the A/B testing methods in my current work and it is going very well. However, I am running into an issue when trying to use the synthetic control method on a geo lift test that was done earlier in the year.

I have weekly sales and ad spend (for a single channel) for each geo. Some geos were exposed to ad spend, some went dark with $0 spend, and some were excluded (I did not design the experiment - just trying to get some value out of it). In my initial model I summed up all the control geos into a single “control_response”. I did the same with treatment. I then created a Synthetic Control Model with a reasonably high r^2, but is clearly under predicting and over stating the causal impact. Below are some charts to describe this.

Here is my code to create the model. It’s simple.

```
# filter data to June 2023 and after
df_response_filtered = df_response_pooled[df_response_pooled.index >= '2023-06-01']
# Model the aggregate geo as a linear combination of the untreated units
# with no intercept parameter.
formula = """response_treatment ~ 0 + response_control"""
# Run the analysis
result = cp.SyntheticControl(
df_response_filtered,
treatment_time,
formula=formula,
model=cp.pymc_models.WeightedSumFitter(
sample_kwargs={"target_accept": 0.95, "random_seed": 42}
),
)
```

Can you please provide any guidance to get the model fitting better? I’m new at this and trying to provide a reasonable estimate of ROAS for this media channel.