Max Knobbout
Currently employed as the lead data scientist in the central data science team at Just Eat Takeaway.com. I have a research background in AI (PhD., MSc.), and am very passionate about applying these techniques to solve complex real-world problems.
Sessions
When A/B testing is not possible but we are still interested in drawing causal conclusions from our data, we need to resort to quasi-experimental approaches. This is the landscape that Just Eat Takeaway.com is navigating in, where we often have experimental data about a specific city, and are interested in knowing what the effects would be on another city. When we drop the requirement of causality and are merely interested in generating likely scenarios, we can use the power of predictive modelling to our advantage. From predicting likely future scenarios, to generating synthetic order data on a minute-to-minute basis, all is possible using the right statistical tools. Even in the absence of pure experimental data, we are still able to model likely futures. This talk is relevant for data scientists that are interested in the intersection of statistics and predictive modelling, and some basic knowledge about these topics will be assumed. The first half of the presentation (0-15) will talk about quasi-experimental models, the second half (15-30) will talk about scenario and data generation.