Relatedly, Benjamin Todd did an analysis which supports the importance of cause prioritisation. It concludes differences in cost-effectiveness within causes are smaller than previously thought:
Overall, my guess is that, in an at least somewhat data-rich area, using data to identify the best interventions can perhaps boost your impact in the area by 3–10 times compared to picking randomly, depending on the quality of your data.
This is still a big boost, and hugely underappreciated by the world at large. However, it’s far less than I’ve heard some people in the effective altruism community claim.
One limitation of Ben’s analysis is that it only looked into nearterm human-focussed interventions, as there were no good data in other areas.
Nice post, Jack!
Relatedly, Benjamin Todd did an analysis which supports the importance of cause prioritisation. It concludes differences in cost-effectiveness within causes are smaller than previously thought:
One limitation of Ben’s analysis is that it only looked into nearterm human-focussed interventions, as there were no good data in other areas.