Explanation
A Crash Course in Causation: Challenging Causal Effects in Observational Data We’ve all heard the phrase “correlation does not equal causation.” What, then, is the same reason? This course aims to answer that question and more! In 5 weeks, you will learn how to define causal effects, what assumptions about your data and models are necessary, and how to implement and interpret popular statistical methods. Students will have the opportunity to apply these methods to example data in R (a free statistical software environment). At the end of the course, students should be able to: 1. Define causal effects using probabilistic outcomes 2. Explain the difference between association and causality with instrumental variables, inverse probability of treatment weights and be familiar with modern statistical methods of estimation Causal effects are indispensable in many areas of education!
What will you learn?
- Change in Equipment
- Propensity Score Comparison
- Understanding the Cause
- The reason
Specificity of Accident Course Causality: Disentangling Causal Effects from Observational Data
- Publisher: Course
- Teacher: Jason A Roy
- Language : English
- Level: Medium
- Number of courses: 5
- Duration : 3 weeks 6 hours per week
The essence
Requirements
- No previous experience is required
Pictures
Sample Clip
Installation Guide
Extract files and watch your favorite player
Subtitle : English
Quality: 720p
Download Links
Password file: free download software
file size
1.27 GB