2021-05-03, 11:30–12:15, PyData Track 1
Most data scientists are focused on predictive (aka supervised) projects, yet the real growth is usually in the estimation of action effects and optimizations of action policies. To this end, I will present causal inference and related packages.
There are three layers of analytics: descriptive (BI), predictive (supervised modeling), and prescriptive - the latter, the less-known one, focus on answering the most important business questions. For example, "what was the effect of giving a discount" ( or "what should I do to create the desired effect" - In this talk, we will first discuss what frameworks are used to answer these questions, namely causal inference, and reinforcement learning. Then we will deep dive into CI and discuss in causality crash 101 courses why is it important. Last but not least we will present existing causal-inference open-source packages and their limitations.