4.7
stars
457 ratings

Jason A. Roy, Ph.D.

33,667 already enrolled

Offered By

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

University of Pennsylvania

About this Course

35,444 recent views

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!  
Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods.  Learners will have the opportunity to apply these methods to example data in R (free statistical software environment).

At the end of the course, learners should be able to:
1.  Define causal effects using potential outcomes
2.  Describe the difference between association and causation
3.  Express assumptions with causal graphs
4.  Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting)
5.  Identify which causal assumptions are necessary for each type of statistical method

So join us.... and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study!

Flexible deadlines

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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

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Skills you will gain

Instrumental Variable
Propensity Score Matching
Causal Inference
Causality

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

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.72/5 (113 Ratings)

Jason A. Roy, Ph.D.

Professor of Biostatistics

Department of Biostatistics and Epidemiology

33,667 Learners

1 Course

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.

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Syllabus - What you will learn from this course

Content Rating91%(1,423 ratings)

Week

Week 1

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)

Week

Week 2

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)

Week

Week 3

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

Observational studies15m

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

Matching30m

Propensity score matching30m

Data analysis project - analyze data in R using propensity score matching30m

Week

Week 4

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

4.7

149 reviews

5 stars
77.07%
4 stars
18.89%
3 stars
2.12%
2 stars
0.84%
1 star
1.06%

TOP REVIEWS FROM A CRASH COURSE IN CAUSALITY: INFERRING CAUSAL EFFECTS FROM OBSERVATIONAL DATA

by WFNov 24, 2018

This course is quite useful for me to get quick understanding of the causality and causal inference in epidemiologic studies. Thanks to Prof. Roy.

by AGFeb 21, 2021

A great start for those starting to explore causal inference. The somewhat dry delivery of the lectures is fully compensated by how clear and informative they are.

by OEJul 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.

by YZDec 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.

View all reviews

Frequently Asked Questions

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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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

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

About this Course

Skills you will gain