Causal Inference

Personalizing Discounts you own this product

This project is part of the liveProject series Causal Inference and Personalization
prerequisites
basics of Python • basics of data science
skills learned
implement causal models for personalization • evaluate the performance of your causal model using experimental data
Matheus Facure
1 week · 6-8 hours per week · INTERMEDIATE
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liveProject This project is part of the liveProject series Causal Inference and Personalization liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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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.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Matheus Facure
Matheus Facure is an economist, data scientist and causal inference specialist at Nubank. He currently works with adding causal inference capabilities to machine learning models with application in the credit card business, and acts as a consultant for other business areas inside Nubank, such as personal loans and marketing.

prerequisites

This liveProject is for data scientists with knowledge of Python, machine learning, and statistics. To begin this liveProject you will need to be familiar with the following:


TOOLS
  • Basics of Python
  • Basics of pandas
  • Basics of Matplotlib
  • Basics of NumPy
TECHNIQUES
  • Basics of data science and machine learning
  • Basics of causal inference

you will learn

In this liveProject, you’ll learn core skills of causal inference and how to apply them to practical business scenarios.


  • Understand how to frame a personalization problem in terms of causal inference
  • Learn key concepts of personalization, like treatment effect heterogeneity
  • Understand the challenges in evaluating a causal model
  • Implement causal models for personalization
  • Evaluate the performance of your causal model using experimental data

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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