The investigation of the effect of the built environment in a neighbourhood and how it impacts residents' health is of value to researchers from public health policy to social science. The traditional methods to assess this impact is through surveys which lead to temporally and spatially coarse grained data and are often not cost effective. Here we propose an approach to link the effects of neighbourhood services over citizen health using a technique that attempts to highlight the cause-effect aspects of these relationships. The method is based on the theory of {\em propensity score matching with multiple `doses'} and it leverages existing fine grained open web data. To demonstrate the method, we study the effect of sport venue presence on the prevalence of antidepressant prescriptions in over 600 neighbourhoods in London over a period of three years. We find the distribution of effects is approximately normal, centred on a small negative effect on prescriptions with increases in the availability of sporting facilities, on average. We assess the procedure through some standard quantitative metrics as well as matching on synthetic data generated by modelling the real data. This approach opens the door to fast and inexpensive alternatives to quantify and continuously monitor effects of the neighborhood built environment on population health.