Randomised evaluations
Programmes such as cash transfers or business training are evaluated by randomly assigning participation and comparing outcomes, with pre-registered analysis plans.
GCRI · Economic Development
Impact evaluation
Causal inference and impact evaluation ask whether a policy or programme changed outcomes compared with what would otherwise have happened. In this field they evaluate development programmes, place-based policies and reforms, stating the assumptions behind each estimate.
01Register
Impact evaluation on the record.
Method recordSTEM method
- MethodCausal inference and impact evaluation
- StewardEconomic Development
- Applied inEconomic Development
- Data3 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 economic development and development finance, 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.
Evaluating place-based policies
Difference-in-differences and synthetic control methods compare regions that received investment with similar regions that did not.
Regression discontinuity
Programmes with eligibility thresholds are evaluated by comparing those just above and below the cutoff.
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
Economic development · Impact evaluation · Run R-3759
Parallel trends holdGCRI · Economic Development
Event study
Method card
- Method
- Causal inference and impact evaluation
- Design
- difference in differences
- Units
- 103
- Label
- MODELLED
Robustness checks
| Main specification | 0.16 | Yes |
| Drop early adopters | 0.19 | Yes |
| Synthetic control | 0.03 | 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 |
|---|---|---|
| Programme administrative data | Treatment and outcomes | Published statistics |
| Household and firm surveys | Outcomes | REPORTED |
| Regional statistics | Comparison units | Published statistics |
05Methods and evidence
How impact evaluation 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.
Sensitivity analysis
Which inputs move the result for impact evaluation most, one at a time across their stated ranges.
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
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 |
|---|---|
| Balance and pre-trends | Treatment and comparison groups are checked for balance and parallel pre-trends |
| Pre-registration | Analysis plans are registered before outcomes are seen |
| External validity | Results are compared across contexts before generalising |
Unobserved confounding and spillovers can bias estimates. Effects found in one context may not transfer to another.
07Process
How the process runs.
Process explorer · Process
Impact evaluation: PRISMA 2020 review reporting
10 steps·4 stages
Process
Impact evaluation: 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.
08Connections
Topics that rely on this method.
09Stewardship
Who maintains the method.
Economic Development stewards causal inference and impact evaluation for the Consortium and reviews how it is applied across platforms.
10Questions
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.
GCRIEconomic DevelopmentCountry Systems Risk and ResilienceGlobal Systems Risk and Resilience
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Economic Development
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