causal-model-building-regression

What problem does it solve?

This Skill unit provides guidance for constructing causal regression models, specifically designed to estimate the independent association between a specific exposure and an outcome, while accounting for confounding.

Core Features & Use Cases

• Causal Hypothesis Testing: Formulate and test hypotheses regarding exposure-outcome relationships.
• Confounding Control: Develop models to adjust for confounding variables that could distort the association.
• Adjustment for Mediators and Colliders: Offers guidance on how to handle mediating and colliding variables within the model.
• Use Case: Suppose you are researching whether smoking (exposure) is causally linked to lung cancer (outcome), accounting for factors like age and gender (confounders).

Quick Start

Execute the 'build_causal_model' script, input your variables (exposure, outcome, confounders), and receive the adjusted regression coefficients.