Bayesian Structural Time-Series Models
In causal inference, we want to analyze the effect of a treatment. The treatment can be any action that interacts with the system or environment that we care about, from changing the colors of a button on a website to the release of a product. We have the choice of taking the action (for example, releasing the product), thereby observing the outcome under treatment, or not taking the action, where we observe the outcome under no treatment. This is illustrated in the diagram here:
Figure 9.3: Causal effect of a treatment
In the diagram, an action is taken or not (medicine is administered to a patient), and depending on whether the action is taken we see the patient recovering (cycling) or going into intensive care.
A causal effect is the difference between what happens under treatment and what happens under no treatment. The problem with this is that we can't observe both potential outcomes at the same time.
However,...
