Causal Diagrams
Draw Your Assumptions Before Your Conclusions
Causal diagrams have revolutionized the way in which researchers ask: What is the causal effect of X on Y? They have become a key tool for researchers who study the effects of treatments, exposures, and policies. By summarizing and communicating assumptions about the causal structure of a problem, causal diagrams have helped clarify apparent paradoxes, describe common biases, and identify adjustment variables. As a result, a sound understanding of causal diagrams is becoming increasingly important in many scientific disciplines.
The first part of this course is comprised of seven lessons that introduce causal diagrams and its applications to causal inference. The first lesson introduces causal DAGs, a type of causal diagrams, and the rules that govern them. The second, third, and fourth lessons use causal DAGs to represent common forms of bias. The fifth lesson uses causal DAGs to represent time-varying treatments and treatment-confounder feedback, as well as the bias of conventional statistical methods for confounding adjustment. The sixth lesson introduces SWIGs, another type of causal diagrams. The seventh lesson guides learners in constructing causal diagrams.
The second part of the course presents a series of case studies that highlight the practical applications of causal diagrams to real-world questions from the health and social sciences.
Professor Photo Credit: Anders Ahlbom
What you'll learn
- How to translate expert knowledge into a causal diagram
- How to draw causal diagrams under different assumptions
- Using causal diagrams to identify common biases
- Using causal diagrams to guide data analysis
Get a Reminder
| Rating | 5.0★ based on 2 ratings |
|---|---|
| Length | 9 weeks |
| Effort | 9 weeks, 2–3 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $99 |
| From | Harvard University, HarvardX via edX |
| Instructor | Miguel Hernán |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Mathematics |
| Tags | Data Analysis & Statistics Math Health & Safety |
Get a Reminder
Similar Courses
What people are saying
elements as time went
But the course started with absolute basics and added more elements as time went on, so I was able to continue.
dag or structural causal
this class is really good to review and begin to learn about causal DAG or structural causal inference !
background in data science
I finished the course with a surprisingly good grade, since I have absolutely no background in data science.
added more elements
lots of repetition
It was very well done, with clear explanations, great graphics (not snazzy, but clear and understandable) and lots of repetition.
very well done
absolute basics
fairly easy
I would guess those who have a reason to take this course - data scientists - would find it fairly easy.
great graphics
idea what
I had no idea what this was when I signed up, but the teaser vid was interesting so thought I'd take a quick peek.
graphics ( not snazzy
Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
International Students and Researchers Committee Member $52k
CAUSAL BUYER AT GRAND & POLY $74k
CAUSAL BUYER AT GRAND & POLY $74k
Write a review
Your opinion matters. Tell us what you think.
Please login to leave a review
| Rating | 5.0★ based on 2 ratings |
|---|---|
| Length | 9 weeks |
| Effort | 9 weeks, 2–3 hours per week |
| Starts | On Demand (Start anytime) |
| Cost | $99 |
| From | Harvard University, HarvardX via edX |
| Instructor | Miguel Hernán |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science Mathematics |
| Tags | Data Analysis & Statistics Math Health & Safety |
Similar Courses
Sorted by relevance
Like this course?
Here's what to do next:
- Save this course for later
- Get more details from the course provider
- Enroll in this course
