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Essential Causal Inference Techniques for Data Science
  • Level Beginner
  • Duration 3 hours
  • Course by Coursera Project Network
  • Offered by

About

Data scientists often get asked questions related to causality: (1) did recent PR coverage drive sign-ups, (2) does customer support increase sales, or (3) did improving the recommendation model drive revenue? Supporting company stakeholders requires every data scientist to learn techniques that can answer questions like these, which are centered around issues of causality and are solved with causal inference. In this project, you will learn the high level theory and intuition behind the four main causal inference techniques of controlled regression, regression discontinuity, difference in difference, and instrumental variables as well as some techniques at the intersection of machine learning and causal inference that are useful in data science called double selection and causal forests. These will help you rigorously answer questions like those above and become a better data scientist!

Modules

Essential Causal Inference Techniques for Data Science

    • 1 Readings
    • 1 Plugin
    • 1 Lab
    • 2 Assignment
Show info about module content

2 Assignment

  • Causal Inference Practice Quiz
  • Causal Inference Graded Quiz

1 Labs

  • Causal Inference Guided Project

1 Readings

  • Project Overview

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Dive into "Essential Causal Inference Techniques for Data Science" to master key causal inference methods crucial for answering complex data-driven questions. Spanning 180 minutes, this beginner-level course covers controlled regression, regression discontinuity, difference in difference, and instrumental variables, along with advanced techniques like double selection and causal forests. Ideal for data scientists aiming to enhance their analytical skills, this course is available for free on Coursera.

Vinod Bakthavachalam
Instructor

Vinod Bakthavachalam