"Correlation isn't Causation" - A causal explainer-Causal Analysis Simplified

Example

Analyzing studies to distinguish between correlation and causation. For instance, if a study finds a link between coffee consumption and increased productivity, this explainer can assess whether the study design allows for causal conclusions or merely shows an association.

Scenario

A user presents a paper claiming that coffee consumption causes increased productivity. This explainer would examine the study's methodology, such as randomized control trials or regression discontinuity designs, to evaluate the validity of the causal claim.

Example

Providing intuitive explanations and analogies to clarify statistical concepts. An example would be explaining the concept of confounding variables using the ice cream and shark attacks scenario, demonstrating how external factors can create an illusion of causation.

Scenario

A student struggling to understand why correlation does not imply causation. Through analogies and simple examples, the explainer makes these concepts accessible, enhancing the student's comprehension of statistical reasoning.

Example

Advising on the application of causal inference principles in real-world research. For example, guiding a public health researcher on how to design a study to explore the causal impact of a new diet on blood pressure.

Scenario

A researcher aims to determine whether a new diet causes a reduction in blood pressure. The explainer would suggest appropriate methodologies, such as randomized control trials or natural experiments, to ensure the research design can support causal conclusions.