Up to this point, we have talked about predictive models. The main purpose of a predictive model is to recognize and forecast. The explanation behind the model's reasoning is of lower priority. On the contrary, causal inference tries to explain relationships in the data rather than to make predictions about the future events. In causal inference, we check whether an outcome of some action was not caused by so-called confounding variables. Those variables can indirectly influence action through the outcome. Let's compare causal inference and predictive models through several questions that they can help to answer:
- Prediction models:
- When will our sales double?
- What is the probability of this client buying a certain product?
- Causal inference models:
- Was this cancer treatment effective? Or is the effect apparent only because of the...
