Identify what works, for whom, and by how much.
We evaluate interventions using rigorous behavioural science, experimental design and applied econometrics—providing reliable evidence on impact, cost-effectiveness and pathways to scale.
What we do:
- Measure impact with confidence: We design fit-for-purpose evaluation strategies—from rapid tests to fully powered experiments—to answer not just whether something works, but why and for whom. Our approaches cover randomised controlled trials, quasi-experimental designs and mixed-methods evaluations tailored to operational realities.
- Build counterfactuals that hold up: Using advanced causal inference methods (e.g., matching, DiD, synthetic controls, regression discontinuity), we construct robust comparison groups that isolate the true effect of an intervention—separating impact from noise, seasonality and structural shifts.
- Capture real-world behaviour: We combine administrative data, digital traces, survey responses and qualitative insights to understand how people actually behave in context. We analyse mechanisms, heterogeneity and behavioural spillovers to uncover how outcomes emerge across different groups.
- Quantify economic and social value: We go beyond traditional cost–benefit metrics to capture the full value an intervention creates. Our evaluations integrate social return on investment (SROI), distributional effects, behavioural spillovers and long-term externalities—providing a comprehensive picture of how programmes affect people, systems and communities. Through economic modelling and equity-sensitive valuation, we show not only what works, but the wider social value generated per euro invested.
- Deliver clear, evidence-ready reporting: We produce rigorous evaluation outputs that distil complex analyses into precise, decision-ready insights. Our reports, technical annexes and policy briefs set out what worked, what did not, effect sizes, key mechanisms and implications for future delivery.
Methods we draw on:
- Randomized controlled trials (RCTs) and field experiments
- Quasi-experimental designs (RDD, DiD, IV, matching, synthetic controls)
- Policy impact evaluations using causal inference
- Monitoring frameworks and real-time data pipelines
- Cost-benefit analysis with behavioral considerations
- PPP and infrastructure project assessments
Who it’s for:
Governments evaluating programs, EU grant recipients, public agencies, social enterprises, businesses measuring ESG impact
What our clients receive:
- Impact evaluation reports with clear, interpretable causal estimates
- Effect size and social value-for-money metrics for decision-making and budgeting
- Heterogeneity and mechanism insights showing which populations benefit most
- Scalability and implementation recommendations grounded in evidence
- Monitoring & Evaluation frameworks with indicators, data plans and learning cycles
- Policy briefs and stakeholder-ready summaries to communicate results clearly