Causal Inference with DAGs in Pediatric Clinical Research

🎯 Learning Objectives

By the end of this guide, you’ll be able to:

✅ Classify variables into confounders, mediators, colliders, and effect modifiers ✅ Detect bias paths using DAGs ✅ Use DAGitty to build and test your DAG ✅ Apply causal logic to real-world clinical studies

🩺 Clinical Question (New Example)

Does initiating antipyretic treatment within 30 minutes reduce the risk of seizure recurrence in children presenting with febrile seizure?

DEPTh Type: Therapeutic (intervention → outcome)

📌 1. DAG = Directed Acyclic Graph

What’s a DAG?

Directed: Arrows show assumed causality
Acyclic: No loops allowed—no feedback
Graph: A visual model of assumed causal structure

🧠 DAGs map your thinking, not just your variables.

🧠 2. Classifying Variable Roles

Role	Meaning	Adjust?	Example
Exposure (X)	The treatment or risk factor	—	Antipyretic within 30 minutes
Outcome (Y)	The endpoint	—	Seizure recurrence within 6 hours
Confounder	Common cause of X and Y	✅	Initial fever severity
Mediator	Lies on causal path X → Y	❌*	Rate of temperature decline
Collider	Caused by both X and Y	❌	ED length of stay
Effect Modifier	Alters strength of X → Y	➕	Underlying epilepsy
Precursor	Precedes X or Y, not necessarily a cause	⚠️ Care	Prior hospitalization

*Adjust for mediator only if estimating direct effect.

🧪 3. Drawing the DAG

dagitty

dag {

FeverSeverity -> EarlyAntipyretic

FeverSeverity -> SeizureRecurrence

EarlyAntipyretic -> TempDecline

TempDecline -> SeizureRecurrence

Epilepsy -> SeizureRecurrence

Epilepsy -> EarlyAntipyretic

ED_Crowding -> EarlyAntipyretic

}

✅ Adjust for: FeverSeverity, Epilepsy

❌ Don’t adjust for: TempDecline (mediator), ED_Crowding (precursor), LengthOfStay (collider)

🔗 Try it: Open DAG in DAGitty

🔁 4. Understanding Path Types

Path Type	Example	Open?	Adjust?	Why?
Causal	X → Y	✅	❌	This is the path you want to estimate
Backdoor (confounder)	X ← C → Y	✅	✅	Biases effect estimate
Mediated causal path	X → M → Y	✅	❌*	Keep open if estimating total effect
Collider path	X → Z ← Y	❌	❌	Adjusting opens a spurious path

🧠 Collider adjustment = new bias creation, not removal.

🧱 5. Backdoor Control

🎯 Backdoor paths = non-causal routes that simulate effects.

To close them:

Identify confounders on these paths
Adjust using regression, matching, and stratification
Validate with DAGitty’s “Adjustment Sets”

In our case:

EarlyAntipyretic ← FeverSeverity → SeizureRecurrence
EarlyAntipyretic ← Epilepsy → SeizureRecurrence ✅ Adjusting for FeverSeverity and Epilepsy blocks both paths.

💥 6. Collider Trap in Pediatric Context

New Scenario:

Let’s add:

dagitty

LengthOfStay -> EarlyAntipyretic

LengthOfStay -> SeizureRecurrence But wait — that's wrong. LengthOfStay is affected by both:

dagitty

dag {

EarlyAntipyretic -> LengthOfStay

SeizureRecurrence -> LengthOfStay

}

Here, LengthOfStay is a collider. ❌ Adjusting for it opens a backdoor: EarlyAntipyretic → LOS ← SeizureRecurrence

🎭 7. Effect Modification vs. Interaction

Concept	What It Means	Example
Effect Modifier	X → Y effect varies by Z	Antipyretic works better in children without epilepsy
Interaction	X1 and X2 together influence Y differently	High fever + fast antipyretic = exponential benefit

Model it with: X + Z + X*Z Visualize it by: Stratified DAGs or subgroup KM curves

🧩 8. Clinical Reasoning First: Then Model

Use this workflow:

Clinical Question → Variable Roles → DAG → DAGitty Adjustment → Regression Model

❌ Don’t start with a dataset ✅ Start with causal structure

📋 9. DAG Construction Checklist

Define exposure (X) and outcome (Y)
Identify variable roles:
- Confounders
- Mediators
- Colliders
- Precursors
Draw DAG (DAGitty or pen + paper)
Run Adjustment Sets in DAGitty
Build a regression model with justified covariates

✅ Final Takeaways

DAGs ≠ illustrations — they are logic blueprints for your research.
Adjust only confounders; avoid colliders unless modeling effect mediation.
DAGitty helps verify if your adjustment set is both valid and minimal.
Model causal paths, not just correlations.
DEPTh logic ensures alignment with clinical decision-making.