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!
Offered By
A Crash Course in Causality: Inferring Causal Effects from Observational Data
University of PennsylvaniaAbout this Course
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Learner Career Outcomes
33%
started a new career after completing these courses
19%
got a tangible career benefit from this course
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
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Intermediate Level
Approx. 18 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Skills you will gain
Instrumental VariablePropensity Score MatchingCausal InferenceCausality
Learner Career Outcomes
33%
started a new career after completing these courses
19%
got a tangible career benefit from this course
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Approx. 18 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Offered by
University of Pennsylvania
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
Syllabus - What you will learn from this course
4 hours to complete
Welcome and Introduction to Causal Effects
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.
4 hours to complete
8 videos (Total 128 min)
2 hours to complete
Confounding and Directed Acyclic Graphs (DAGs)
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.
2 hours to complete
8 videos (Total 86 min)
5 hours to complete
Matching and Propensity Scores
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.
5 hours to complete
12 videos (Total 171 min)
3 hours to complete
Inverse Probability of Treatment Weighting (IPTW)
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.
3 hours to complete
9 videos (Total 119 min)
Reviews
TOP REVIEWS FROM A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA
by WLMar 16, 2019
Very easy to follow examples and great coverage for such an important topic! The delivery sometimes get repetitive and I wish we talked more about how the uncertainties are derived.
by JCNov 20, 2020
A high quality course that delivers what it says in the title. Well-paced introduction to the potential outcomes framework, with a nice balance of theoretical and practical aspects.
by MFDec 27, 2017
I really enjoyed this course, the pace could be more even in parts. Sometimes the pace could be more even and some more books/reference material for further study would be nice.
by FFNov 29, 2017
The material is great. Just wished the professor was more active in the discussion forum. Have not showed up in the forum for weeks. At least there should be a TA or something.
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