GiveWell still relies a lots on the their explicit cost-effectiveness numbers. Elie Hassenfeld, their co-founder and CEO, mentioned on the Clearer Thinking podcast that:
GiveWell cost- effectiveness estimates are not the only input into our decisions to fund malaria programs and deworming programs, there are some other factors, but theyâre certainly 80% plus of the case.
The numerical cost-effectiveness estimate in the spreadsheet is nearly always the most important factor in our recommendations, but not the only factor. That is, we donât solely rely on our spreadsheet-based analysis of cost-effectiveness when making grants.
We donât have an institutional position on exactly how much of the decision comes down to the spreadsheet analysis (though Elieâs take of â80% plusâ definitely seems reasonable!) and it varies by grant, but many of the factors we consider outside our models (e.g. qualitative factors about an organization) are in the service of making impact-oriented decisions. See this post for more discussion.
For a small number of grants, the case for the grant relies heavily on factors other than expected impact of that grant per se. For example, we sometimes make exit grants in order to be a responsible funder and treat partner organizations considerately even if we think funding could be used more cost-effectively elsewhere.
âI feel that any giving approach that relies only on estimated expected-value â and does not incorporate preferences for better-grounded estimates over shakier estimates â is flawed. Thus, when aiming to maximize expected positive impact, it is not advisable to make giving decisions based fully on explicit formulas. Proper Bayesian adjustments are important and are usually overly difficult to formalize.â
One can estimate the expected value using sceptical priors to weight uncertain estimates less heavily, as with inverse-variance weighting. I think it is good to be explicit, so I suppose the question is whether it is cost-effective, i.e. worth it to invest time to formalise the Bayesian adjustment.
Thanks for the answer, Karolina!
GiveWell still relies a lots on the their explicit cost-effectiveness numbers. Elie Hassenfeld, their co-founder and CEO, mentioned on the Clearer Thinking podcast that:
GiveWell also commented:
One can estimate the expected value using sceptical priors to weight uncertain estimates less heavily, as with inverse-variance weighting. I think it is good to be explicit, so I suppose the question is whether it is cost-effective, i.e. worth it to invest time to formalise the Bayesian adjustment.