Workshop Agenda, Tuesday, May 3, 2022
Please register to attend the workshop. This event is past, and registration is closed. Videos are available for on-demand viewing.
|Time (Pacific)||Session Title|
|9:05am||Title: Update on Microsoft causal open-source libraries|
Speakers: Eleanor Dillon, Cheng Zhang, Amit Sharma, Chao Ma, Darren Edge
Abstract: In this talk, we will present Microsoft’s open-source causal tools and how they work together. We’ll review recent updates to DoWhy and EconML and introduce new tools DECI and ShowWhy. DECI (Deep End-to-end Causal Inference) combines causal discovery and inference; and ShowWhy provides a no-code interface to make causal inference easier for data analysts.
|9:30am||Fireside chats with domain experts, hosted by Emre Kıcıman|
|10:00am||Title: Supercharge your A/B testing with automated causal inference. |
Speaker: Egor Kraev, Head of AI, Wise
Abstract: An A/B test consists of splitting the customers into a test and a control group, and choosing a large enough sample size to observe the average treatment effect (ATE) we are interested in, in spite of all the other factors driving outcome variance. With causal inference models, we can do better than that, by estimating the effect conditional on customer features (CATE), thus turning customer variability from noise to be averaged over to a valuable source of segmentation, and potentially requiring smaller sample sizes as a result. Unfortunately, there are many different models available for estimating CATE, with many parameters to tune and very different performance. In this talk, we will present our auto-causality library, which combines the three marvelous packages from Microsoft – DoWhy, EconML, and FLAML – to do fully automated selection and tuning of causal models based on out-of-sample performance, just like any other AutoML package does. We will describe the projects inside Wise currently starting to apply it, and present rather striking results on comparative model performance and out-of-sample segmentation on Wise CRM data.
|10:20am||Breakout discussions, by industry use cases|