Causal Diagrams: Draw Your Assumptions Before Your Conclusions

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Causal Diagrams: You’ll learn simple graphical rules that allow you to use intuitive pictures, improve study design and data analysis for causal inference.

Last updated on June 17, 2021 11:20 am

Introduction

In this course on Causal Diagrams, you’ll learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal inference.

About this course

Causal diagrams (DAGs) 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, such diagrams have helped clarify apparent paradoxes, describe common biases, and identify adjustment variables. As a result, a sound understanding of causal DAGs is becoming increasingly important in many scientific disciplines.

The first part of this course is comprised of seven lessons that introduce causal DAGs and their 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 diagram. The seventh lesson guides learners in constructing causal DAGs.

The second part of the course presents a series of case studies that highlight the practical applications of causal DAGs to real-world questions from the health and social sciences.

What you will learn from this Course on Causal Diagrams?

  • 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 DAGs to guide data analysis.

Syllabus on Causal Diagrams

1. Course Introduction

2. Lesson 1: Causal DAGs

3. Lesson 2: Confounding

4. Lesson 3: Selection Bias

5. Lesson 4: Measurement Bias / Putting it all together

6. Lesson 5: Time-Varying Treatments

7. Lesson 6: Causal SWIGs

8. Section 7: Building Your DAG

9. Bonus: Causal Diagram Development

10. Cases

11. End of Course Survey

Note: Your review matters 

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

$99.00

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  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Beginner
  • 1-3 Months
  • Free Course (Affordable Certificate)
  • English
  • Data Analysis Research methodology
Expert Score
9.8
Learner rating
9.8
PROS: No need to have background in data science. Started with absolute basics and added more elements as time went on. Well-organized with clear explanations, great graphics and lots of repetition. Very good to review and begin to learn about causal DAG or structural causal inference. Concise teaching with case studies and packed with great material.
CONS: Found the course difficulty to be medium. It could be a little abstract for those without experience in the area. Need more elaboration on some course topics.

Description

Introduction

In this course on Causal Diagrams, you’ll learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal inference.

About this course

Causal diagrams (DAGs) 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, such diagrams have helped clarify apparent paradoxes, describe common biases, and identify adjustment variables. As a result, a sound understanding of causal DAGs is becoming increasingly important in many scientific disciplines.

The first part of this course is comprised of seven lessons that introduce causal DAGs and their 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 diagram. The seventh lesson guides learners in constructing causal DAGs.

The second part of the course presents a series of case studies that highlight the practical applications of causal DAGs to real-world questions from the health and social sciences.

What you will learn from this Course on Causal Diagrams?

  • 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 DAGs to guide data analysis.

Syllabus on Causal Diagrams

1. Course Introduction

2. Lesson 1: Causal DAGs

3. Lesson 2: Confounding

4. Lesson 3: Selection Bias

5. Lesson 4: Measurement Bias / Putting it all together

6. Lesson 5: Time-Varying Treatments

7. Lesson 6: Causal SWIGs

8. Section 7: Building Your DAG

9. Bonus: Causal Diagram Development

10. Cases

11. End of Course Survey

Note: Your review matters 

If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Beginner
  • 1-3 Months
  • Free Course (Affordable Certificate)
  • English
  • Data Analysis Research methodology

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Causal Diagrams: Draw Your Assumptions Before Your Conclusions
Causal Diagrams: Draw Your Assumptions Before Your Conclusions

$99.00

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