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 a decrease of -34.3%. The 90%
interval of this percentage is [-43.4%, -23.4%].


This means that the negative effect observed during the intervention
period is statistically significant.
If the experimenter had expected a positive effect, it is recommended
to double-check whether anomalies in the control variables may have
caused an overly optimistic expectation of what should have happened
in the response variable in the absence of the 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/.
