Workflows
This section offers practical guidance for using the Tetrad interface to explore data and perform causal analysis.
The workflow pages introduce recommended steps for inspecting datasets, forming assumptions, choosing methods, and systematically evaluating causal models.
- Causal Analysis Workflows
- Data Exploration: Understanding Your Data Before Causal Discovery
- 1. Load and Inspect Your Data
- 2. Review Variable Types
- 3. Examine Marginal Distributions with Histograms
- 4. Explore Pairwise Relationships with the Plot Matrix
- 5. Consider Linearity and Gaussianity (Informally)
- 6. Reflect on Causal Sufficiency and Latent Variables
- 7. Clarify Your Modeling Goals
- 8. Moving Forward
- Practical Notes
- Algorithm Selection and Assumptions
- Manual Exploration: Try Searches Interactively
- Running Searches and Grid Search Tips
- Model Evaluation and Markov Checking
- Interpreting Results
- 1. What a Discovered Graph Represents
- 2. Types of Output and Their Meaning
- 3. Interpreting Common Edge Marks
- 4. Robustness and Stability
- 5. What You Can Say (With Care)
- 6. What You Should Avoid Saying Unqualified
- 7. Using Background Knowledge
- 8. Communicating Uncertainty Clearly
- 9. Documenting Your Analysis
- 10. Summary
- 🧭 What’s Next
- Example: Auto MPG Analysis with Grid Search