frontier research deep techcausal inferencedo calculuseconmluplift modeling
Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Theoretical command
35% weight
Probe command of potential outcomes and structural causal models: identification via backdoor and front-door criteria, instrumental variables, difference-in-differences, and where ignorability assumptions break.
Evidence to listen for
Explains the underlying theory at the level the role demands, and can go a layer deeper when pushed
Knows which results are established and which are contested
Distinguishes their own contribution from the field's
Comfortable saying where the theory runs out
Five-point scoring guide
1
Poor
Recites terminology without understanding; cannot go one layer deeper.
2
Needs Improvement
Surface familiarity; conflates established results with speculation.
3
Satisfactory
Solid grasp of the core theory; thin at the frontier.
4
Very Good
Strong command; separates settled results from open questions.
5
Excellent
States identification assumptions before touching estimators, distinguishes Pearl and Rubin framings fluently, and names conditions that invalidate each design.
02
Evaluation factor
From theory to hardware or code
30% weight
Ask what they built: DoWhy or EconML pipelines, double machine learning estimators, causal forests, refutation tests, or a production uplift model driving treatment targeting.
Evidence to listen for
Has built, simulated, or run something real, not only published about it
Knows the gap between the idealised model and the actual apparatus or system
Names the practical constraint that dominates in real conditions
Can describe a result that did not match prediction
Five-point scoring guide
1
Poor
Purely theoretical; no contact with implementation.
2
Needs Improvement
Some exposure but unaware of practical constraints.
3
Satisfactory
Has implemented work; understands the main real-world limits.
4
Very Good
Strong practical record; articulate about theory-versus-reality gaps.
5
Excellent
Walks through code they wrote, cites effect sizes with confidence intervals, and shows the refutation or placebo tests they ran.
03
Evaluation factor
Research judgement
20% weight
Test how they choose between an experiment, a synthetic control, and observational adjustment when randomisation is blocked by cost, ethics, or interference between units.
Evidence to listen for
Chooses problems by tractability and value, not novelty alone
Knows when to abandon a line of work
Reads and evaluates others' results critically
Can say what would falsify their own approach
Five-point scoring guide
1
Poor
Chases novelty; no sense of tractability or when to stop.
2
Needs Improvement
Weak problem selection; persists past the point of value.
3
Satisfactory
Reasonable judgement within a defined programme.
4
Very Good
Selects problems well and knows when to abandon a line.
5
Excellent
Chooses designs against data constraints, abandons unidentifiable questions early, and explains sensitivity analysis bounds rather than claiming point precision.
04
Evaluation factor
Explaining it to non-specialists
15% weight
Judge how they brief product or clinical stakeholders who read correlation as causation, including how they present confounding, external validity, and what the estimate cannot support.
Evidence to listen for
Explains the work to an engineer, an executive, or a funder without either mystifying or dumbing it down
Writes clearly
Collaborates across disciplines
Makes the case for resources in terms the audience cares about
Five-point scoring guide
1
Poor
Cannot communicate outside their specialism.
2
Needs Improvement
Explanation is either impenetrable or hollow.
3
Satisfactory
Adequate with technical peers; less effective with lay audiences.
4
Very Good
Explains clearly to specialists and non-specialists alike.
5
Excellent
Translates ATE and CATE into decisions without jargon, states caveats plainly, and pushes back on overclaiming in others' analyses.
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