How I frame causal questions
A vague question produces vague evidence.
The easiest way to improve a causal analysis is often to improve the question.
Here is the framing I use when I want a causal question that is actually answerable.
The five-part causal question
A well-formed causal question usually specifies:
- Intervention: what is being changed?
- Outcome: what is being measured?
- Population: for whom?
- Time window: when do we expect effects?
- Counterfactual: compared to what?
Let’s walk through each part.
1) Intervention: what changes?
Not “exposure” in the abstract — a change you could imagine implementing.
Bad: - “Does education affect income?”
Better: - “What is the effect of completing secondary school (vs not completing) on income?”
Even better: - “What is the effect of a policy that increases secondary school completion by X% on income at age 30?”
You don’t always need the last level of detail, but you do need something that implies a real comparison.
2) Outcome: what exactly is measured?
Outcomes are often underspecified.
Bad: - “Does the program reduce poverty?”
Better: - “Does the program reduce the probability of being below the poverty line?”
Even better: - “Does the program reduce the probability of being below the poverty line 12 months after enrollment?”
Also ask: is the outcome measured consistently, and does measurement itself change after the intervention?
3) Population: for whom?
Effects are not universal constants.
Bad: - “Does remote work increase productivity?”
Better: - “Among software developers in mid-sized firms, does remote work increase measured output?”
Sometimes the biggest difference between studies is not methods — it’s population.
4) Time window: when do we expect effects?
Effects vary over time. Many arguments silently assume “immediate and permanent.” That’s rarely true.
Ask: - What is the short-run effect? - What is the long-run effect? - Are there adaptation dynamics?
In policy settings, time is a major confounder: trends, shocks, and seasonality can look like treatment effects.
5) Counterfactual: compared to what?
This is the heart of causal inference.
Every causal estimate is a comparison between: - what happened under the intervention - what would have happened without it
So you must define the “without it.”
Examples: - no policy change - alternative policy - delayed adoption - business-as-usual care
A surprising number of disagreements are really disagreements about counterfactuals.
The “estimand sentence”
When I want to be precise, I write a one-sentence estimand:
“Among [population], what is the effect of [intervention] versus [comparison] on [outcome] measured over [time window]?”
If you can’t write this sentence, your study design will likely be unstable.
A worked example
Vague: - “Do nutrition protocols improve ICU outcomes?”
Framed: > “Among adult ICU patients, what is the effect of implementing an early enteral nutrition protocol (vs standard practice) on 28-day mortality and ICU length of stay, measured during the admission and up to 28 days after ICU entry?”
Now you can design: - what counts as protocol exposure - what comparison is fair - what timing matters - what confounding is plausible
Why this matters
Framing does three things immediately:
- It forces you to be honest about what you can claim.
- It tells you what data you need.
- It makes your identification strategy easier to evaluate.
The fastest way to improve your causal inference is to treat question framing as a first-class step, not a formality.
If you want, send me a question you’re working on and I’ll rewrite it into a well-formed causal question plus 2–3 plausible counterfactuals.