Analysis report {CausalImpact}


During the post-intervention period, the response variable had
an average value of approx. 5.3. 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.
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 a decrease of -34.3%. The 90%
interval of this percentage is [-43.4%, 23.4%].
This means that, although it may look as though the intervention has
exerted a negative effect on the response variable when considering
the intervention period as a whole, this effect is not statistically
significant and so cannot be meaningfully interpreted.


The apparent effect could be the result of random fluctuations that
are unrelated to the intervention. This is often the case when the
intervention period is very long and includes much of the time when
the effect has already worn off. It can also be the case when the
intervention period is too short to distinguish the signal from the
noise. Finally, failing to find a significant effect can happen when
there are not enough control variables or when these variables do not
correlate well with the response variable during the learning period.


The probability of obtaining this effect by chance is p = 50%.
This means the effect may be spurious and would generally not be
considered statistically significant.


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