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A causal diagram is a graphical tool that enables the visualisation of causal relationships between variables in a causal model. A casual diagram allows you to summarise and communicate assumptions about the causal structure of a problem.

The [course_title] course teaches the theory of causal diagrams and illustrates its applications to causal inference.  Then you will be studied a series of case studies that highlight the practical applications of causal diagrams to real-world questions from the health and social sciences.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit:Harvard University

Course Curriculum

Module : 01
Causal Diagrams 00:02:00
Video Syllabus 00:02:00
Association vs. Causation 00:03:00
1. Estrogens and Uterine Cancer: The problem 00:05:00
2. What is a DAG? 00:05:00
Cause and Effect 00:09:00
Confounding 00:09:00
5. Selection Bias 00:10:00
D-separation 00:08:00
Estrogens and Uterine Cancer: The Explanation 00:07:00
Module : 02
Folic Acid and Birth Defects: The Problem 00:04:00
Definition of Confounding 00:06:00
Confounders (Part I) 00:09:00
Confounders (Part II) 00:07:00
Confounders (Part III) 00:07:00
Confounding Control 00:10:00
Folic Acid and Birth Defects: The Explanation 00:06:00
Smoking and Dementia: The Problem 00:02:00
Definition of Structural Bias 00:04:00
Studies with Outcome-based Selection 00:07:00
Module : 03
Follow-up Studies (Part I) 00:07:00
Follow-up Studies (Part II) 00:06:00
Selection Bias Control 00:07:00
Smoking and Dementia: The Explanation 00:04:00
Hepatitis B Vaccine and Multiple Sclerosis: The Problem 00:03:00
Definition of Measurement Bias 00:05:00
Classification of Measurement Error 00:07:00
Measurement Bias Control 00:07:00
Hepatitis B Vaccine and Multiple Sclerosis: The Solution 00:06:00
Naming the Bias 00:05:00
Module : 04
Why Causal Inference is Hard 00:03:00
Antiretroviral Therapy and Human Immunodeficiency Virus: The Problem 00:03:00
Time-Varying Treatments 00:06:00
Time Varying Confounding 00:05:00
Treatment-Confounder Feedback 00:04:00
Bias Conventional Methods 00:06:00
Antiretroviral Therapy and Human Immunodeficiency Virus: The Explanation 00:05:00
Jamie Robins Interview 00:16:00
Part 1 00:05:00
Part 2 00:03:00
Part 3 00:06:00
Module : 05
Part 4 00:03:00
Part 1 00:06:00
Part 2 00:05:00
Part 3 00:07:00
Part 4 00:11:00
Part 1 00:08:00
Part 2 00:06:00
Part 3 00:06:00
Part 4 00:09:00
Part 1 00:09:00
Part 2 00:09:00
Part 3 00:04:00
Part 4 00:01:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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