Analysis report {CausalImpact}


During the post-intervention period, the response variable had
an average value of approx. 5.3. By contrast, in the absence of an
intervention, we would have expected an average response of 4.3.
The 90% interval of this counterfactual prediction is [3.3, 6.3].
Subtracting this prediction from the observed response yields
an estimate of the causal effect the intervention had on the
response variable. This effect is 3.3 with a 90% interval of
[2.3, 6.3]. For a discussion of the significance of this effect,
see below.


Summing up the individual data points during the post-intervention
period (which can only sometimes be meaningfully interpreted), the
response variable had an overall value of 10.3.
By contrast, had the intervention not taken place, we would have expected
a sum of 9.3. The 90% interval of this prediction is [8.3, 9.3].


The above results are given in terms of absolute numbers. In relative
terms, the response variable showed an increase of +41.0%. The 90%
interval of this percentage is [23.4%, 43.4%].


This means that the positive effect observed during the intervention
period is statistically significant and unlikely to be due to random
fluctuations. It should be noted, however, that the question of whether
this increase also bears substantive significance can only be answered
by comparing the absolute effect (3.3) to the original goal
of the underlying intervention.


The probability of obtaining this effect by chance is very small
(Bayesian one-sided tail-area probability p = 0.05).
This means the effect is statistically significant. It can be
considered causal if the model assumptions are satisfied.


For more details, including the model assumptions behind the method, see
https://google.github.io/CausalImpact/.
