Essential Causal Inference Techniques for Data Science

4.7
별점
16개의 평가
제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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
Beginner초급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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!

개발할 기술

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

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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.

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

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ESSENTIAL CAUSAL INFERENCE TECHNIQUES FOR DATA SCIENCE의 최상위 리뷰

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