Causal Inference

Estimating the Profitability of Discounts you own this product

This free project is part of the liveProject series Causal Inference and Personalization
prerequisites
basics of Python • basics of data science
skills learned
how bias gets in the way of causal inference • adjust for bias using a linear regression
Matheus Facure
1 week · 2-4 hours per week · INTERMEDIATE
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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.

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. A basic understanding of causal inference will also be helpful, but is not required. 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
  • Basics of linear regression

you will learn

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


  • Understand why association does not imply causation and what does
  • Learn key concepts of causal, like inference potential outcomes, causal graphs and confounding bias
  • Understand how bias gets in the way of causal inference
  • Understand how to adjust for bias using a linear regression

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