I'm employing Google Correlate time series to evaluate the causal impact of an intervention on a variable of interest y. I made sure that the Google series are highly correlated with y during the pre-treatment period, and that their relationship with y is merely spurious. Because of this, the probability of these series being affected by the intervention can be thought to be very low.
The causal impact analysis reveals a significant impact in the expected direction.
When employing a placebo treatment period (I divide the pre-treatment period into two: placebo pre-treatment and placebo treatment), and obtaining Google Correlate variables that are good predictors for the placebo pre-treatment period (to avoid a downwards bias), I find that the model fails to find a significant impact where there was no intervention.
So it seems like the spurious variables behave as good predictors would. Given this, is it a bad practice to employ such variables for causal impact analysis because of the spurious nature of their correlation?