What will you learn in HarvardX: Causal Diagrams: Draw Your Assumptions Before Your Conclusions course
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Understand the fundamentals of causal reasoning and why correlation is not causation.
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Learn how to use causal diagrams (Directed Acyclic Graphs – DAGs) to represent assumptions clearly.
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Identify confounders, mediators, and colliders in causal relationships.
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Learn how causal diagrams guide correct data analysis and study design.
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Avoid common analytical mistakes that lead to biased or incorrect conclusions.
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Strengthen critical thinking for research, data analysis, and evidence-based decision-making.
Program Overview
Introduction to Causal Thinking
⏳ 1–2 weeks
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Learn why causal reasoning matters in data analysis and research.
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Understand the difference between association and causation.
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Explore real-world examples of misleading conclusions from data.
Building Causal Diagrams (DAGs)
⏳ 2–3 weeks
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Learn the components of causal diagrams: nodes, arrows, and directionality.
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Translate real-world assumptions into clear causal graphs.
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Practice drawing DAGs for common analytical problems.
Confounding, Mediation, and Bias
⏳ 2–3 weeks
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Identify confounders and understand how they bias estimates.
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Learn about mediators and why adjusting for them can be problematic.
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Understand colliders and how incorrect control introduces bias.
Using Causal Diagrams in Analysis
⏳ 2–3 weeks
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Learn how DAGs guide variable selection for analysis.
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Understand adjustment strategies to estimate causal effects correctly.
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Apply causal reasoning to observational data scenarios.
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Job Outlook
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Highly valuable for Data Analysts, Data Scientists, Researchers, and Policy Analysts.
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Essential for roles involving observational data, impact evaluation, and research design.
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Widely applicable in healthcare, economics, social sciences, and public policy.
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Strengthens analytical credibility and decision-making accuracy.
