Essential Causal Inference Techniques for Data Science

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Dans ce Projet Guidé, vous :

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
BeginnerDébutant
CloudAucun téléchargement requis
VideoVidéo en écran partagé
Comment DotsAnglais
LaptopOrdinateur de bureau uniquement

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!

Les compétences que vous développerez

  • Regression Discontinuity Design
  • Causal Inference
  • Instrumental Variable
  • regression
  • Difference In Differences

Apprendrez étape par étape

Votre enseignant(e) vous guidera étape par étape, grâce à une vidéo en écran partagé sur votre espace de travail :

  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.

Comment fonctionnent les Projets Guidés

Votre espace de travail est un bureau cloud situé dans votre navigateur, aucun téléchargement n'est requis.

Votre enseignant(e) vous guide étape par étape dans une vidéo en écran partagé

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