We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more!
A Crash Course in Causality: Inferring Causal Effects from Observational Data
A Crash Course in Causality: Inferring Causal Effects from Observational Data
Instructor: Jason A. Roy, Ph.D.
Access provided by EDGE Group
46,398 already enrolled
Gain insight into a topic and learn the fundamentals.
569 reviews
Intermediate level
Some related experience required
Flexible schedule
2 weeks at 10 hours a week
Learn at your own pace
90%
Most learners liked this course
Skills you'll gain
- Statistical Analysis
- R Programming
- Data Analysis
- Regression Analysis
- Graph Theory
- Research Design
- Statistical Methods
- Statistical Inference
- R (Software)
- Statistical Modeling
- Quantitative Research
- Skills section collapsed. Showing 10 of 11 skills.
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Assessments
16 assignments
Taught in English
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There are 5 modules in this course
This module focuses on defining causal effects using potential outcomes. A key distinction is made between setting/manipulating values and conditioning on variables. Key causal identifying assumptions are also introduced.
What's included
8 videos3 assignments
This module introduces directed acyclic graphs. By understanding various rules about these graphs, learners can identify whether a set of variables is sufficient to control for confounding.
What's included
8 videos2 assignments
An overview of matching methods for estimating causal effects is presented, including matching directly on confounders and matching on the propensity score. The ideas are illustrated with data analysis examples in R.
What's included
12 videos5 assignments
Inverse probability of treatment weighting, as a method to estimate causal effects, is introduced. The ideas are illustrated with an IPTW data analysis in R.
What's included
9 videos3 assignments
This module focuses on causal effect estimation using instrumental variables in both randomized trials with non-compliance and in observational studies. The ideas are illustrated with an instrumental variables analysis in R.
What's included
9 videos3 assignments
Instructor
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Showing 3 of 569
YZ
Reviewed on Dec 14, 2021
It will be better to give reviews of related applications in specific AI areas (e.g, computer vision, NLP, etc.) at the end of each of the sections of the lesson.
GB
Reviewed on Mar 11, 2021
Excellent video lectures. Challenging end of module quizzes. I found more challenging doing the practical exercises because I had no experience with R.
OD
Reviewed on Jul 29, 2020
I enjoyed the course a lot and I think I took a lot from it as well. The quizzes and computer projects were appropriate, and the resourcees posted were very useful.
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