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

with  Jason A. Roy, Ph.D.
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!

Syllabus

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.

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.

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.

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.

Instrumental Variables Methods
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.

0 Student
reviews
Cost Free Online Course (Audit)
Pace Upcoming
Provider Coursera
Language English
Certificates Paid Certificate Available
Calendar 5 weeks long

Disclosure: To support our site, Class Central may be compensated by some course providers.

Earn a Master's Degree Online

from

FAQ View All
What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

Reviews for Coursera's A Crash Course in Causality: Inferring Causal Effects from Observational Data Based on 0 reviews

• 5 star 0%
• 4 star 0%
• 3 star 0%
• 2 star 0%
• 1 star 0%

Did you take this course? Share your experience with other students.

Class Central

Get personalized course recommendations, track subjects and courses with reminders, and more.