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

  • Writer: Mayta
    Mayta
  • 11 hours ago
  • 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.

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