Policy makers permanently struggle for designing policies that have positive impact on well-being of population. Before making decisions on launching and scaling up policy interventions, they need sound evidence on what works and what does not.
As an experimental methodology, randomized controlled trial is the best scientific approach to estimate causation between policy interventions (treatments) and their effects. This type of evaluation provides objective picture of results, serving as a detailed and well-grounded bases for decision-making.
Trials are implemented through randomizing the treatment across similar targets. Treatment and control groups are being selected with purpose of estimating net effect of interventions reducing systematic problems of selection and bias from confounding variables.
Experts at R-Insights highly recommend randomized trials to measure causation, in particular, to:
Measure behavioral effects of interventions to facilitate guidance of future interventions and priorities
Evaluate the impact of a particular intervention with the intention of deciding on similar interventions or to determining whether to terminate or continue the intervention