Essential Steps to Unveil the Structure Behind Cause and Effect

Master the Journey of Developing Causal DAGs for Clarity and Insights

In our journey on Casualty, we navigated through “The Traps of Blindly Trusting Data: Simpson’s Paradox,” explored “An Intuitive Introduction to Causal Inference,” and “Visualizing Causality Using Causal Graphs.” We examined “How Biases Impact Causal Inference” and discussed strategies on “How to Control Causal Inference Bias”

Now, we continue the journey to understand how to depict the impact of Aspirin (A) on Stroke (Y) through a Causal DAG(Directed Acyclic Graph) in an observational study.

Our focus is on understanding and drawing a causal relationship between the usage of Aspirin and its impact on the risk of experiencing a Stroke.

We started with Simpson’s Paradox, highlighting the dangers of drawing conclusions from combined data without accounting for group differences. It emphasizes the importance of identifying confounding factors that can influence both the treatment and the outcome, possibly obscuring or even reversing the true causal relationship between variables.

Association alone does not imply causation.

Following the discussion on correlation and causation, we explored Causal inference, a fundamental concept in statistics, epidemiology, economics, and other disciplines focused on determining how interventions or exposures influence outcomes.