University of Pennsylvania
A Crash Course in Causality: Inferring Causal Effects from Observational Data
University of Pennsylvania

A Crash Course in Causality: Inferring Causal Effects from Observational Data

Taught in English

Some content may not be translated

40,739 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.7

(532 reviews)

Intermediate level
Some related experience required
18 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace

Details to know

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Assessments

16 quizzes

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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 quizzes

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 quizzes

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 quizzes

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 quizzes

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 quizzes

Instructor

Instructor ratings
4.7 (135 ratings)
Jason A. Roy, Ph.D.
University of Pennsylvania
1 Course40,739 learners

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Recommended if you're interested in Probability and Statistics

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4.7

532 reviews

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AA
5

Reviewed on May 15, 2018

WL
4

Reviewed on Mar 16, 2019

FF
5

Reviewed on Nov 29, 2017

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