Pricing Engine: Estimating Causal Impacts in Real World Business Settings

The explosion of data science in modern technology firms has created a new class of workers with the technical backgrounds needed to solve a wide array of statistical problems using a diverse set of machine learning (ML) techniques. However, the most important decisions made by such firms are typically policy questions such as How much should we invest in R&D?, Should we cut prices?, or Which product would benefit most from an aggressive marketing campaign?. These are all questions that hinge on understanding the causal effect of various policy interventions and, as such, cannot be answered (or even well-informed) by purely statistical approaches. Instead, they require econometric techniques that can yield answers with a clear causal interpretation. Causal inference is about understanding the true effect of a treatment, call it'D', on an outcome, call it'Y '. How would Y change if we changed D? ML on the other hand is usually about building a good predictor function of Y using many features X (that may include D).