Alternative Approaches to A/B Experiments - 3 Causal Impact Approaches | Jennifer Vlasiu

Make answering ‘what if’ analysis questions a whole lot easier by learning about state-of-the-art, end-to-end applied frameworks for causal inference.
We will cover:
Microsoft’s “Do Why” Package Causal Impact in Python - DoWhy | An end-to-end library for causal inference - DoWhy | An end-to-end library for causal inference documentation (microsoft.github.io)
Bayesian Causal Impact in R
MLE Causal Impact in Python
Bonus: AA Testing, when to use and why it matters
We will apply these models in the context of understanding the impact of a marketing rewards campaign, as well as understand the impact from a product/feature upgrade
This workshop was conducted by Jennifer Vlasiu, Data Science & Big Data Instructor at York University
Useful resources for this workshop:
- bit.ly/github_...
Learn more about WiDS Workshops: widsconference.org/workshops
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