Did more street patrols cause a decrease in crime?
Did a new school cause an increase in home prices?
Did a new tax policy cause an increase in entrepreneurship?

All these questions share one thing in common – they address causality not correlation.

Using data to identify causal effects requires more than a background in statistics, it requires in-depth knowledge about the policy or program one is trying to evaluate.

The key to estimating causal effects is being able to find the relevant counterfactual what would have happened had we not allocated this intervention when and where we did?

Knowing something about the policy; about its incentives and how it might induce people to act, can reveal powerful natural or quasi-experimental research designs.

Urban Spatial specializes in locating these mechanisms and using them to estimate the costs and benefits of a given intervention.

See it in action!