  Airlines have been making use of highly complex Revenue Management Systems to
maximize revenue for decades. Estimating the impact of changing one component
of those systems on an important outcome such as revenue is crucial, yet very
challenging. It is indeed the difference between the generated value and the
value that would have been generated keeping business as usual, which is not
observable. We provide a comprehensive overview of counterfactual prediction
models and use them in an extensive computational study based on data from Air
Canada to estimate such impact. We focus on predicting the counterfactual
revenue and compare it to the observed revenue subject to the impact. Our
microeconomic application and small expected treatment impact stand out from
the usual synthetic control applications. We present accurate linear and
deep-learning counterfactual prediction models which achieve respectively 1.1%
and 1% of error and allow to estimate a simulated effect quite accurately.
