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How to Think Causally in Clinical Research — The Counterfactual + DAG Blueprint

  • Writer: Mayta
    Mayta
  • 2 days ago
  • 3 min read
“Causation isn’t just about what happened—it’s about what would have happened instead.”

🎯 The Clinical Dilemma

Your patient, Mr. G, age 50, has stage IV lung cancer. He receives an herbal treatment and survives.

Question: Did the herb save him?

Or would he have survived anyway?

That’s the counterfactual question. And it’s the gold standard for causal inference in medicine.


🔄 1. Association ≠ Causation

Let’s separate noisy thinking from causal clarity.

Concept

Meaning

Association

Knowing X helps predict Y

Causation

Changing X will change Y

Example:

“Patients who take Vitamin D have better COVID outcomes.”That’s association.
“Vitamin D improves COVID outcomes.”That’s a causal claim—and it needs counterfactual proof.

🧬 2. The Counterfactual Logic

Key Concept:

A causal effect = Observed outcome minus Counterfactual outcome

But we never observe both for the same person. You only see one timeline: treated or untreated.

So, how do we estimate the missing outcome?

🧪 3. Approximating the Counterfactual: Your Toolbox

We create “what-if” worlds using:

Tool

Function

RCTs

Create two comparable worlds via randomization

Matching

Pair treated patients with similar untreated ones

Stratification

Compare within subgroups (e.g., ECOG = 2)

Regression

Adjust for confounders statistically

Propensity Scores

Balance groups based on treatment likelihood

DAGs

Show where bias might sneak in (more below)


🔁 4. The DAG: Your Bias-Detecting Blueprint

What is a DAG?

A Directed Acyclic Graph is a visual map of your assumptions:

  • Arrows = causal directions

  • Nodes = variables

  • Helps decide what to adjust for

✍️ Example DAG: Does the Herbal Drug Help?

Age → Herbal Treatment → Survival ↘-------------------------↗

  • Age affects both treatment and survival → confounder

  • Must adjust to isolate the drug’s effect.

🛑 Don't Adjust for These:

Role

Rule

Example

Confounder

Adjust ✅

Age, ECOG

Mediator

Don’t adjust if estimating total effect ❌

Side effects

Collider

Never adjust ❌

ER admission caused by both severity and treatment

🔍 Secret Insight: Adjusting for colliders introduces false associations.DAGs protect you from this silent sabotage.

🎲 5. Mr. G’s Real-World Counterfactual

You want to answer:

Would Mr. G have survived without the herbal treatment?

Since we can’t observe that:

  • Define precise inclusion/exclusion: age, cancer stage, ECOG.

  • Find matched patients who didn’t receive the drug.

  • Adjust for known confounders (e.g., smoking).

If survival differs → credible evidence for causality.

🔄 6. Time Matters: Temporality

Causal claims demand:

  • Cause precedes effect

  • No reverse logic

If treatment happens after improvement, it can’t be the cause.

⚔️ 7. Confounding, Indication Bias & Comparability

Real-world mess:

  • Doctors prescribe based on prognosis = confounding by indication

  • Sickest avoid treatment = confounding by contraindication

  • Self-selection = selection bias

How to beat this:

  • Design with comparability in mind

  • Use DAGs to plan adjustments

  • Use propensity scores, matching, or inverse probability weighting

📘 Occurrence Equation for Causal Studies

🧠 Summary Table

Element

Function

Caution

Counterfactual

The unseen alternative

Estimate, don’t assume

DAG

Bias map

Build before analyzing

Confounder

Adjust ✅

Blocks bias

Mediator

Adjust ❌ if estimating the total effect


Collider

Never adjust ❌

Causes a spurious association

RCT

Gold standard

Randomizes counterfactual


✅ Final Takeaways

  • Causal inference = imagining alternate realities and estimating them scientifically.

  • Counterfactual thinking is not optional—it’s the foundation.

  • DAGs are essential for identifying what’s safe (and unsafe) to adjust.

  • Comparability is key, and it can be achieved through design or statistics.

  • Always clarify the variable roles: confounder, mediator, and collider.

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