05-03, 11:30–12:15 (Asia/Jerusalem), 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.
English
Target audience –Data Scientists, Managers
Other (target audience) –business analytics
Hanan is a data scientist at Vianai Systems where he develops methods to optimize business outcomes by using ML (causal inference, bandits, and RL). He is alumni of successful startups such as BioCatch.com and Gong.io where he showed proof of concept and built the data science teams from scratch.
He is also an alumnus of cooperates such as Microsoft where he was a senior data scientist. During his army service, Hanan was a signal processing and digital communication team leader (IDF).
Hanan holds a Ph.D. in computation neuroscience (Hebrew University) specialized in computational modeling of behavior and neural activity. He holds also a B.Sc. [cum laude] in Physics, B.Sc. [summa cum laude] and M.Sc. [cum laude] in Electrical Engineering (Tel Aviv University)