Matching and Weighting Methods for Causal Inference

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Why Matching and Weighting Methods for Causal Inference?

One of the biggest challenges data scientists face is having sparse data to answer your question. This course will introduce you to some of the most common techniques for balancing data into treatment and control groups, estimating treatment effects, and making the most of the data that you do have.

Take-Away Skills

In this course, you will learn how to use matching, weighting, and stratification techniques to prepare the data for causal analysis. You will be able to calculate propensity scores for unobserved data with observed data, and you will learn how to balance treatment and control groups. You will learn how to use the data that we have to estimate what we don’t.

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    Learn about matching and weighting methods including stratification and propensity scores!

What you'll create

Portfolio projects that showcase your new skills

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How you'll master it

Stress-test your knowledge with quizzes that help commit syntax to memory

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testimonial

— Madelyn, Pinterest

I know from first-hand experience that you can go in knowing zero, nothing, and just get a grasp on everything as you go and start building right away.

Course Description

Use matching, weighting, propensity scores, and stratification to prepare data for causal analysis.

Details

Earn a certificate of completion
2 hours to complete in total
Advanced

Learn about matching and weighting methods including stratification and propensity scores!