Analytical Epidemiology · Biases, Confusion and Effect Modification

Introduction

What this guide covers

Introduction Introduction Introduction Chart from the original guide

Overview

Topics covered

• Introduction
• Bias, Confusion and Effect Modification
• Causation and the counterfactual framework
• The conditions of causal identification
• Directed Acyclic Graphs (DAGs) and d-separation
• Taxonomy of error in epidemiology

Related concepts

Main connections

• Causal inference & potential outcomes
• Epidemiological measures & risk ratios
• Logistic regression
• Confounding & selection bias
• Supervised machine learning
• Diagnostic test evaluation & ROC

Compatible notation

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