In this series of liveProjects, you’ll explore a variety of causal inference techniques to help optimize the discounting strategy of an e-commerce business. Causal inference is a groundbreaking field of data science that’s breaking out of academic offices and into practical application across industries. It provides a mathematical basis for determining how one variable (the “treatment”) will impact another, allowing predictions of how certain actions might affect business outcomes and KPIs.
This is a really strong liveProject on a subject that I think more data scientists should learn more about. I'm very happy this exists! Overall, the liveProject acts as a great hands-on companion to go along with the author's book.
In this liveProject, you’ll utilize causal inference techniques to help an e-commerce company estimate the impact of discounts on profits. You’ll learn how bias gets in our way of inferring the effect of discounts, and how to adjust for it for more accurate results. Leverage adjustment techniques, like linear regression, to adjust for bias and see how linear regression can improve the quality of your data. Your final task is to present a recommendation on whether the company should distribute its discounts or not.
In this liveProject, you’ll use regression discontinuity design (RDD) as a form of natural experiment that works as an alternative to A/B testing. RDD measures treatment effects at points of discontinuity to get an idea of the effectiveness of a program without needing to rigorously A/B test it. You’ll use this technique to assess the discounting strategy of an e-commerce company, in a way that will allow you to start applying causal inference to other practical problems.
In this liveProject, you’ll use difference-in-differences as an alternative to A/B testing to assess the discounting strategy of an e-commerce company. This approach compares growth between a group that has been experimented on, and data from a control group. You’ll use Python and causal inference to examine the growth trajectory in profits from customers in a high discounts group against customers that didn’t get a discount.
In this liveProject, you’ll use causal inference to investigate data on randomized discounts and determine if an e-commerce company should offer personalized discounting. You’ll estimate a different treatment effect for each customer in the hopes to see if some are positive, and figure out which customers should get what discounts. You’ll utilize Python and machine learning to build this personalization system, and implement a causal model for personalization.
In this liveProject, you’ll utilize machine learning for treatment effect estimation in order to help an e-commerce company deliver targeted discounting to the most profitable customers. You’ll build a model that predicts the effect of a discount on a customer following a causal model, maximizing profits for the business.
The liveProject has a good mix of hands-on exercises with solid conceptual prerequisites from the author's own book. The liveProject benefits from having an intuitive dataset/business problem to apply the causal inference techniques to. The author also takes the effort to set up the problem statement well.
This liveProject series is for data scientists with knowledge of Python, machine learning, and statistics. A basic understanding of causal inference will also be helpful, but is not required. To begin these liveProjects you will need to be familiar with the following:
In this liveProject, you’ll learn core skills of causal inference and how to apply them to practical business scenarios.
geekle is based on a wordle clone.