Description
A Crash Course in Causality: Inferring Causal Effects from Observational Data is published by Coursera Academy. We have all heard the phrase “correlation does not equal causation”. So what is equal causation? The purpose of this course is to answer this question and more! During a 5-week course you will learn how causal effects are defined, what assumptions are required about your data and models, and how to implement and interpret some popular statistical methods. Students will have the opportunity to use these methods on sample data in R (a free statistical software environment). At the end of the course, students will be able to: 1. Define causal effects using potential outcomes. 2. Describe the difference between association and causation. 3. Express hypotheses with causal graphs 4. Implement several types of causal inference methods (e.g. matching, instrumental variables, treatment inverse probability weighting) 5. Identify which causal assumptions are necessary for each type of statistical method Discover why the method Modern statistics are essential for estimating causal effects in many fields of study
What you will learn
- Instrumental variable
- Propensity score matching
- Causal inference
- causality
A Crash Course in Causality: Inferring Causal Effects from Observational Data
- Publisher: Coursera
- teacher : Jason A. Roy
- English language
- Education level: Intermediate
- Number of courses: 5
- Duration of training: 6 weeks including 3 hours of work per week
Head of the seasons
Course prerequisites
- No previous experience necessary
Pictures
Sample video
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
download link
File(s) password: www.downloadly.ir
Size
1.27 GB
Be the first to comment