Essential Causal Inference Techniques for Data Science

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In this Guided Project, you will:

Learn the limitations of AB testing and why causal inference techniques can be powerful.

Understand the intuition behind and how to implement the four main causal inference techniques in R.

Explore newer methods at the intersection of causal inference and machine learning and implement them in R.

Clock2 hours
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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!

Skills you will develop

Regression Discontinuity DesignCausal InferenceInstrumental VariableregressionDifference In Differences

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Use Controlled / Fixed Effects Regression to estimate impact of customer satisfaction on customer revenue.

  2. Use Regression Discontinuity to estimate the impact of customer support on renewal probability.

  3. Use Difference in Difference to estimate the impact of raising prices on revenue.

  4. Use Instrumental Variables to see whether using the mobile app leads to increased customer retention.

  5. Use Double Selection to speed up AB tests and get more precise estimates.

  6. Use Causal Forests to find heterogeneous treatment effects separated by registration source for impact of discounts.

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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