I expect they are paid 2x more than trained statisticians unfortunately. Happily though, they didn’t try to solve this problem with Judea Pearl-style causal inference hype.
A better approach to this would probably be treating the data as panel data and either doing monthwise hierarchical Bayesian regressions, and testing if the coefficients pooled over time are significantly non-zero, or else using a sort of distributed lag model or instrumental variables model on the monthwise simple regressions. In these cases, it’s better to roll up lagged features of the exogenous time series to serve as constructed covariates in the lag model, than to rely on differencing and time series tests which are like a landmine of interpretation issues.
Checking causality in models like this is very tricky, so a lot of care has to be taken, and you should expect work to proceed slowly and require a lot of posterior checks.
A better approach to this would probably be treating the data as panel data and either doing monthwise hierarchical Bayesian regressions, and testing if the coefficients pooled over time are significantly non-zero, or else using a sort of distributed lag model or instrumental variables model on the monthwise simple regressions. In these cases, it’s better to roll up lagged features of the exogenous time series to serve as constructed covariates in the lag model, than to rely on differencing and time series tests which are like a landmine of interpretation issues.
Checking causality in models like this is very tricky, so a lot of care has to be taken, and you should expect work to proceed slowly and require a lot of posterior checks.