DAGs vs. Causal Diagrams: What to Use for Clinical Causal Inference

Mayta
May 7
2 min read

🎯 TL;DR Summary Table

Feature	DAG (Directed Acyclic Graph)	Causal Diagram (General)
✅ Arrows only	Yes – all edges are directional	Often, but may include non-directional paths
🔁 No cycles	Yes – strictly acyclic	Can have feedback loops or dynamic components
🧠 Logic model	Purely structural assumptions	Broader – includes feedback, timing, processes
📐 Graph theory-based	Yes – rooted in Pearl’s structural causal model	Sometimes informal or illustrative
🔬 Research use	Adjustment, backdoor paths, DAGitty analysis	Teaching, conceptual modeling, sometimes DAG
🎯 Objective	Identify bias paths and valid adjustment sets	Often to explain a general system

🧪 1. What is a DAG?

✅ Definition

A Directed Acyclic Graph (DAG) is a precise, rule-based model used in causal inference to:

Specify assumptions
Reveal bias paths
Calculate adjustment sets

DAGs only use arrows, and never have feedback loops (acyclic). They are mathematical tools with causal logic and can be tested in tools like DAGitty.

🔬 Example

dagitty

dag {

ShockSeverity -> EarlyFluids

EarlyFluids -> Hypotension

}

Used to test:

Backdoor paths
Minimal sufficient adjustment sets
Mediator vs confounder logic

🧩 2. What is a Causal Diagram?

🧠 Definition

A causal diagram is a broader concept that includes any visual causal system model.

May include:

Loops (feedback, delay)
Non-directional links
Boxes for stages or processes
Time-sequence arrows

Often used in complex systems modeling, conceptual frameworks, or clinical teaching.

📌 Example (non-DAG causal diagram):

Fever → Cytokine Storm → Multi-organ Failure

↑ ↓

Immunosuppression ←—┘

This feedback loop violates DAG rules but is valid in systems thinking.

🔍 3. Why This Distinction Matters in CECS

Use Case	Use a DAG	Use a Causal Diagram
Designing a regression model	✅ To identify confounding paths	❌ Too vague or circular
Teaching system dynamics	❌ Too rigid	✅ Ideal for concept mapping
Planning a target trial	✅ DAGs emulate randomization logic	❌ Can't compute adjustment sets
Modeling feedback in physiology	❌ DAGs can't handle cycles	✅ Systems or influence diagrams

🔍 Secret Insight: In causal inference, precision trumps beauty. DAGs look simple, but what matters is what they let you prove.

🔄 4. When Causal Diagrams Fail as DAGs

Common violations:

Violation	Example	Fix to Make It a DAG
Feedback loop	A → B → A	Remove loop or model time-sequenced DAGs
Unlabeled arrows	A — B (unclear direction)	Specify arrow direction
Ambiguous timing	Diagnosis ↔ Treatment	Break into nodes: "Before diagnosis", etc.
Unstructured effects	Vague arrows “influences”	Use structured paths: X → Y or X ← Z → Y

✅ Final Takeaways

A DAG is a formal, strict subset of causal diagrams, optimized for inference.
Causal diagrams are conceptual tools; DAGs are analytical weapons.
For CECS study design, always build a valid DAG first—use other diagrams for teaching or exploring mechanisms.
Use DAGitty or pen-and-paper to construct DAGs with rules: directional, acyclic, causal paths only.