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
48,235 recent views
Learner Career Outcomes
33%
started a new career after completing these courses
27%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
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
27%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your 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)
8 videos
Confusion over causality19m
Potential outcomes and counterfactuals13m
Hypothetical interventions17m
Causal effects19m
Causal assumptions18m
Stratification23m
Incident user and active comparator designs14m
3 practice exercises
Practice Quiz30m
Practice Quiz30m
Causal effects30m
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)
8 videos
Causal graphs9m
Relationship between DAGs and probability distributions15m
Paths and associations7m
Conditional independence (d-separation)13m
Confounding revisited9m
Backdoor path criterion15m
Disjunctive cause criterion9m
2 practice exercises
Practice Quiz30m
Identify from DAGs sufficient sets of confounders30m
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)
12 videos
Overview of matching12m
Matching directly on confounders13m
Greedy (nearest-neighbor) matching17m
Optimal matching10m
Assessing balance11m
Analyzing data after matching20m
Sensitivity analysis10m
Data example in R16m
Propensity scores11m
Propensity score matching14m
Propensity score matching in R15m
5 practice exercises
Practice Quiz30m
Practice Quiz30m
Matching30m
Propensity score matching30m
Data analysis project - analyze data in R using propensity score matching30m
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)
9 videos
More intuition for IPTW estimation9m
Marginal structural models11m
IPTW estimation11m
Assessing balance9m
Distribution of weights9m
Remedies for large weights13m
Doubly robust estimators15m
Data example in R26m
3 practice exercises
Practice Quiz30m
IPTW30m
Data analysis project - carry out an IPTW causal analysis30m
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
What will I get if I purchase the Certificate?
Is financial aid available?
More questions? Visit the Learner Help Center.
