Free
CourseLearn the Basics of Causal Inference with R
Learn how to use causal inference to figure out how different variables influence your results.
This course includes
This course includes
Skill level
IntermediateTime to complete
Average based on combined completion rates — individual pacing in lessons, projects, and quizzes may vary5 hoursProjects
3Prerequisites
1 courseWe suggest you complete the following courses before you get started with Learn the Basics of Causal Inference with R:- R for Programmers
About this course
Discover the principles of causation and causal inference in this Linear Regression in R course. Learn foundational concepts and advanced techniques like matching, weighting, and difference-in-differences to explore why things happen. Develop skills to isolate variables, work with complex datasets, and interpret your analysis effectively.
Skills you'll gain
Understanding causal inference
Applying linear regression techniques
Analyzing relationships in datasets
Isolating variables for deeper insights
Syllabus
5 lessons • 3 projects • 4 quizzesCertificate of completion available with Plus or Pro
Earn a certificate of completion and showcase your accomplishment on your resume or LinkedIn.
Projects in this course
- practice Project
Impact of Cover Crops on Wheat Crop Yields
In this project, you will use inverse probability of treatment weighting (IPTW) to estimate the causal effect of cover crop usage on wheat crop yields. - practice Project
Effect of Emergency Weather Systems on Transit Times
Apply regression discontinuity design (RDD) to estimate the causal effect of a snow emergency system on average commuting time in a fictitious city. - practice Project
Enhanced Recovery After Surgery
Analyze the effect of a surgical recovery plan on patient outcomes using the Difference in Differences (DID) technique in R.
Meet the creator of the course
Andrea Hassler
Andrea has a Master's in Applied Statistics from NYU and a Bachelor's in Psychology from SUNY New Paltz. She has worked with students individually as a tutor for many years. She has also contributed to research projects in the health sciences as a statistical consultant and research assistant.Earn a certificate of completion
Show your network you've done the work by earning a certificate of completion for each course or path you finish.- Show proofReceive a certificate that demonstrates you've completed a course or path.
- Build a collectionThe more courses and paths you complete, the more certificates you collect.
- Share with your networkEasily add certificates of completion to your LinkedIn profile to share your accomplishments.
Reviews from learners
- The progress I have made since starting to use codecademy is immense! I can study for short periods or long periods at my own convenience - mostly late in the evenings.ChrisCodecademy Learner @ USA
- I felt like I learned months in a week. I love how Codecademy uses learning by practice and gives great challenges to help the learner to understand a new concept and subject.RodrigoCodecademy Learner @ UK
- Brilliant learning experience. Very interactive. Literally a game changer if you're learning on your own.John-AndrewCodecademy Learner @ USA
Our learners work at
Join over 50 million learners and start Learn the Basics of Causal Inference with R today!
Looking for something else?
Related resources
- Article
Linear Regression with scikit-learn: A Step-by-Step Guide Using Python
Discover the fundamentals of linear regression and learn how to build linear regression and multiple regression models using the sklearn library in Python. - Article
Introduction to Regression Analysis
This article is a brief introduction to the formal theory (otherwise known as Math) behind regression analysis. - Article
Log Transformation in Linear Regression: When and How to Use It
Learn when and how to apply log transformations in linear regression to fix skewed data and improve model accuracy. Python examples included.
Related courses and paths
- Free course
Potential Outcomes Framework for Causal Inference
Use the Potential Outcomes Framework to estimate what we cannot measure.Advanced1 hour - Free course
Matching and Weighting Methods for Causal Inference
Use matching, weighting, propensity scores, and stratification to prepare data for causal analysis.Advanced2 hours - Free course
Difference in Differences for Causal Inference
Learn how to use the difference in differences method to estimate effects by analyzing trends over time.Advanced1 hour
Browse more topics
- Math98,936 learners enrolled
- R304,551 learners enrolled
- Data science5,000,695 learners enrolled
- Code foundations8,118,554 learners enrolled
- Computer science6,589,427 learners enrolled
- Web development5,434,404 learners enrolled
- Python4,036,211 learners enrolled
- For business3,833,310 learners enrolled
- JavaScript3,078,434 learners enrolled
Unlock additional features with a paid plan
Practice Projects
Guided projects that help you solidify the skills and concepts you're learning.Assessments
Auto-graded quizzes and immediate feedback help you reinforce your skills as you learn.Certificate of Completion
Earn a document to prove you've completed a course or path that you can share with your network.