Evaluating public health measures
Difference-in-differences and synthetic control designs compare places that adopted a measure with comparable places that did not, and trends before adoption are examined before any effect is reported.
GCRI · Health Nexus
Causal inference for public health
Causal inference asks whether an intervention or exposure actually changed health outcomes, compared with what would otherwise have happened. In this field it evaluates public health measures, service investments and environmental exposures where randomisation is often impossible, and states the assumptions each estimate rests on.
01Register
Causal inference for public health on the record.
Method recordSTEM method
- MethodCausal inference and impact evaluation
- StewardEconomic Development
- Applied inHealth Nexus
- Data4 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What causal inference and impact evaluation does here.
Causal inference and impact evaluation develop methods to determine whether and how interventions or exposures cause outcomes. They separate genuine effects from correlation. In health and biosecurity, causal inference shows which risk reduction measures actually work. It supports allocating resources toward interventions with demonstrated effect across connected risks. The canonical description is on the STEM Commons.
Exposure-response for heat and air
Time-series and case-crossover designs relate daily exposure to outcomes in the same population, controlling for season and trend, to estimate lagged exposure-response functions.
Impact of access investments
New facilities, fee removals or outreach programmes are evaluated with stepped rollout, regression discontinuity or matching, so the effect on use and outcomes is separated from background change.
03Monitor
Impact evaluation.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Impact evaluation
Health · Causal inference for public health · Run R-9605
Parallel trends holdGCRI · Health Nexus
Event study
Method card
- Method
- Causal inference and impact evaluation
- Design
- difference in differences
- Units
- 86
- Label
- MODELLED
Robustness checks
| Main specification | 0.06 | Yes |
| Drop early adopters | 0.10 | Yes |
| Synthetic control | 0.11 | Yes |
04Data
What the method consumes.
Datasets and records used by causal inference and impact evaluation on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Routine health information systems | Outcomes before and after interventions | REPORTED |
| Policy and measure trackers | Timing and stringency of interventions | DECLARED |
| Daily mortality and admissions | Outcome series for exposure studies | Published statistics |
| Reanalysis and air quality fields | Exposure series | MODELLED |
05Sector signature
The sector signature of causal inference for public health.
The sectors causal inference for public health touches and how strongly they depend on each other: what a study has to observe and model together.
Select corners · hover lines6 / 15 relations
Signature
Health and its partners
Health gives workforce for operations to infrastructure (0.3), farm labour to food (0.3) and utility workforce to water (0.2).
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.42 share of total
Strongest relations in play
- Water → Food · irrigation0.8
- Food → Health · nutrition0.7
- Infrastructure → Water · pipes, dams and treatment works0.6
- Water → Health · drinking water and sanitation0.7
06Validation
How results are checked.
Identification assumptions are probed through placebo tests, falsification checks and sensitivity analysis. Replication across settings tests external validity.
| Check | How it is done |
|---|---|
| Pre-trend and placebo tests | Effects are tested in periods and places where none should exist |
| Sensitivity to unmeasured confounding | Bounds or E-values state how strong hidden confounding would need to be to explain the result |
| Replication across settings | Effects are compared across countries or periods before they are generalised |
Unobserved confounding and spillovers can bias estimates. Effects found in one context may not transfer to another.
07Methods and evidence
How causal inference for public health is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Uncertainty bands
How far the outlook for causal inference for public health could spread, shown as ranges rather than a single line.
- Central estimate
- median of the ensemble
- Ranges
- likely and very likely, in IPCC calibrated language
- Ensemble
- members, scenarios and initial conditions stated
- Read with
- the sensitivity of the result to each input
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
08Connections
Topics that rely on this method.
09Process
How the process runs.
Process
Causal inference for public health: PRISMA 2020 review reporting
PRISMA 2020 is a reporting guideline for systematic reviews; the steps follow its checklist, not a mandated sequence, and no Nexus document adopts it.
01 Plan
- 01Registration and protocolRecord · The review teamThe guideline asks for registration details, access to the protocol and any amendments.
- 02Eligibility criteriaRecord · The review teamThe guideline asks for the inclusion and exclusion criteria and how studies were grouped.
02 Find and select
- 03Search: sources and strategiesStep · The review teamThe guideline asks for the information sources and the full search strategies. It does not fix which sources are used.
- 04Selection of studiesStep · The review teamThe guideline asks how studies were judged eligible, including how many reviewers screened. It does not fix the number of reviewers.
- 05Flow diagramRecordThe flow diagram shows the number of records found, the number of studies included and the reasons for exclusion.
03 Analyse
- 06Data collectionStep · The review teamThe guideline asks how data were collected from reports and which data items were sought.
- 07Risk of biasStep · The review teamThe guideline asks for the methods and tools used to assess risk of bias.
- 08SynthesisModel · The review teamThe guideline asks for the effect measures and the synthesis methods. It does not prescribe meta-analysis.
- 09Certainty of evidenceStep · The review teamThe guideline asks for the methods used to assess certainty, and reports the result per outcome. It does not name a grading scale.
04 Report
- 10Review reportRecordThe guideline helps authors report why the review was done, what they did and what they found.
ReportsOutline of an externally published reporting guideline, not a Nexus process and not a record of operation. PRISMA 2020 mainly covers reviews of intervention effects. No Nexus page names it. Every programme is at service level R0.
10Stewardship
Who maintains the method.
Economic Development stewards causal inference and impact evaluation for the Consortium and reviews how it is applied across platforms.
11Connected
Where this page leads across the Consortium.
12Questions
Questions and answers.
What are the limits of causal inference and impact evaluation in this field?
Unobserved confounding and spillovers can bias estimates. Effects found in one context may not transfer to another.
How are results checked?
Identification assumptions are probed through placebo tests, falsification checks and sensitivity analysis. Replication across settings tests external validity.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
ReportsContribute
Contribute evidence from this page.
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
GCRIHealth NexusGlobal Sector Risk and ResilienceCountry Systems Risk and ResilienceEvidence Brief
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