Glossary and Causal DAGs
Reference Materials for Causal Inference
Reference Materials 📊
Comprehensive collection of terminology and causal diagrams for workshop participants
Causal Inference Glossary
📖 Complete Terminology Guide
Access the comprehensive glossary of causal inference terms and definitions used throughout the workshop.
This glossary contains essential definitions for terms including: - Average Treatment Effect (ATE), Conditional Average Treatment Effect (CATE) - Backdoor paths, confounders, and colliders - Instrumental variables and propensity scores - Time-varying confounding and measurement error - Causal estimands, estimators, and identification strategies
Essential reference for understanding causal inference methodology and terminology.
Causal DAGs Reference Collection
Core Terminology & Foundations
1. Foundational Concepts
1a. Local Conventions
Essential local conventions for causal diagram construction and interpretation
1b. Directed Graph Terminology
Core terminology specific to directed acyclic graphs (DAGs)
S1. Graphical Key
Visual reference guide for interpreting causal diagram symbols and notation
Common Questions & Applications
2. Practical Applications
2. Common Causal Questions
Frequently encountered causal questions and how to approach them
6. Effect Modification
Understanding when and how treatment effects vary across subgroups
9. External Validity
Approaches to generalising causal findings across populations and contexts
Time Series & Confounding
3. Keeping Time on Your Side
3. Time Series Approaches
How longitudinal data help address confounding bias
4. Three-Wave Panel Methods
Using three-wave panel data for causal inference
5. Time Series Limitations
When time series approaches may not resolve confounding
S3. Time-Resolved Confounding
Advanced approaches to time-varying confounding
Advanced Topics
4. Complex Methodological Issues
7. Selection Bias Focus
Detailed examination of selection bias in longitudinal studies
8. Measurement Error
Structural approaches to representing and addressing measurement error
10. Experimental Design
How experiments address confounding and selection bias challenges
Supplementary Materials
5. Additional Resources
S5. Timing Examples
Practical examples of confounding and timing issues
S6. Detailed Panel Examples
What can go wrong in a three-wave panel.
S7. Cross-Sectional Approaches When to report multiple DAGs in cross-sectional studies
S8. Bias Correction
Quantitative approaches to bias correction
S9. Mediator Bias
Understanding confounding bias in mediation analysis
S10. Misclassification Bias
Examples of misclassification bias and bias towards the nulls