Firstly, they need to be similar to the treatment group in terms of characteristics that could affect their outcomes regardless of the intervention i.e., addressing the issue of endogeneity. to react to the intervention in a similar way, and should also have similar exposure to other interventions occurring at the same time that could also affect philippines rcs data their outcomes – so for instance, the new national jobs and careers service that was also announced at the same time as the Youth Guarantee.
So, we now have an understanding of what we need to evaluate the impact of an intervention. In the next post I’ll talk about some of the techniques we can use to quantify impact in a case like this.
And finally…
As the Data Impact blog reaches its tenth birthday, we’re also looking forward to the future of data impact and data policy. It’s clear that various forms of machine learning and artificial intelligence will continue to be important, but I wonder what else the next years might bring in developments for data and the impact it can lead to. Exciting times!