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.
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Gain insight into a topic and learn the fundamentals.
4.7
(557 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 18 hours
Learn at your own pace
90%
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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
Instructor ratings
4.7 (145 ratings)Offered by
Recommended if you're interested in Probability and Statistics
University of Illinois Urbana-Champaign
American Psychological Association
Johns Hopkins University
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