Very exciting to see this rolled out! I love the new recommendations page, and I’m thrilled that GWWC is taking the “evaluate the evaluators” mission seriously. The one thing I’m kind of confused by is the new GWWC funds. Don’t EA funds already serve as the natural choice for donors who want high impact giving opportunities within a particular cause area and don’t want to worry about having to manually update their selections as recommendations change? Having a duplicate set of funds within Effective Ventures seems like it will add overhead and confusion without necessarily providing a clear benefit. In trying to think through the potential benefits, I do see how having the GWWC funds would make it possible to not recommend certain EA funds in future years if you were to find issues with their grantmaking. However, it seems like those kinds of issues could also be addressed via the EA funds making changes in response to the GWWC research team’s findings. Having two sets of competing funds trying to do the same thing within EV just appears to me to be a potentially poor use of resources unless there’s a clear justification for keeping them separate. 

My apologies if this question has already been addressed elsewhere, I tried to look back through the previous announcement and AMA but may have missed some discussions. 

I think your BOTEC is unlikely to give meaningful answers because it treats averting a human death as equivalent to moving someone from the bottom of their welfare range to the top of their welfare range. At least to me, this seems plainly wrong - I'd vastly prefer shifting someone from receiving the worst possible torture to the greatest possible happiness for an hour to extending someone's ordinary life for an hour. 

The objections you raise are still worth discussing, but I think the best starting place for discussing them is Duffy (2023)'s model (Causal model, report), rather than your BOTEC.

Great post! Thanks for sharing, and for all you've donated!

This is a really impressive report! Looking at 5 different risk-aversion models and applying them to 7 different interventions is an extraordinarily ambitious task, but I think you really succeeded at it. Almost every question or potential objection I came up with as I was reading ended up getting answered within the text, and I'm very excited to try using some of these models in my own research. 

I wanted to also highlight how helpful it was that you took the time to examine simplified cases and ground some of the results in easy-to-understand terminology. To give just a few examples: 

• The worked example of applying DMREU to a simplified analysis on pages 35-36 was really helpful for actually understanding how DMREU works 
• Continuing to explain risk aversion levels in terms of the n% chance of saving 1000 lives vs. 100% chance of saving 10 lives example was very helpful for understanding what different results meant in practice 
• The example in Table 13 was great for building some intuition on what different values of the WLU coefficient implied

I did also have a few minor comments: 

1. You used DALYs as a unit of utility throughout this analysis. I think that's absolutely fine for this kind of initial report, but it's something I would be concerned about if it persisted in future inter-cause cost-effectiveness comparison models that were actually used to make funding decisions. DALY weights (since the GBD 2010 update) are designed to be strictly measures of health status, not of utility.^[1] My understanding is that interventions that have huge utility benefits for an individual (e.g. pain management in terminal cancer patients) can have little-to-no impact on the DALY weights of their conditions. For that reason, I'm worried about the potential incoherence of trying to translate the benefits of animal welfare interventions into DALY terms, and I think in general that DALY-based CEAs may be very error-prone. I don't think any of those concerns really impact the conclusions of this particular report, but it would have been nice to see some mention of this issue in the limitations section.
2. This may just be me, but I found it quite hard to follow some of the acronyms. In particular, I found that not having the W for "weighted" in REU and DMREU meant that I kept having to go back and look them up. It might be worth considering using RWEU and DMRWEU as the acronyms instead (but definitely see what others say, this is just from n=1).
3. For the current DALY burden of malaria, you say:
   
   "The estimate for the annual DALY burden of malaria used in the REU model comes from Our World in Data’s Burden of Disease dashboard, which places the estimate at 63 million DALYs per year for malaria and neglected tropical diseases. (I assume that this is equal to the DALY burden of just malaria, which is likely inaccurate but probably correct to within an order of magnitude). I add uncertainty to this estimate by modeling the DALY burden of malaria as a normally distributed variable with a mean of 63 million DALYs and a standard deviation of 5 million DALYs"
   
   The raw data is available from the IHME website, with the point estimate being 46,437,811 DALYs due to malaria and the range being 23,506,933 to 80,116,072.

1. ^{^}

   This is not to say that the pre-2010 DALY weights were good measures of utility, just that the post-2010 weights are explicitly not trying to measure it

I'm sure I'm just being dumb here, but I'm confused by this formula:

What's the sum over p(E_j)? Why doesn't it end up just summing to 1? 

Regarding the choice of means, I personally think that the arithmetic mean may make more sense in this case. Suppose you had 3 respondents who gave responses of 0.1, 0.001, and 0.001 as their DALYs from full banning of sugary drinks. If we assume that the individuals are accurately responding regarding their views and (as you are though the rest of this analysis) that utility gains and losses can be linearly aggregated across different individuals, and then the total harm that occurs to the three individuals in the case of a drink ban is 0.102 DALYs. If you had used the geometric mean instead, you would estimate that the total harm from a drink ban was just 0.014 DALYs. 

It seems that an arithmetic mean would make more sense if one thinks that differences between the survey respondents' views reflected true underlying differences in preferences, whereas a geometric mean would make more sense if one thinks that differences between the survey respondents' views reflected noise in estimates of similar underlying preferences. 

Very interesting, thanks for the detailed explanation! I'll be curious to see what Daniel says, but my intuition is that y=x^0.1 is a generous assumption for the shape of the freedom of choice utility curve. I do agree that I'd expect utility losses from freedom of choice to be nonlinear, but the function you're using would imply that 80% of freedom of choice is preserved when sugary drink consumption has been cut by 90%. Moreover, my intuition is that the welfare losses due to reduced sugary drink consumption are also partially due to not getting tasty drinks anymore (thinking for myself, I wouldn't really mind not having sugary drinks, but I would definitely be sad in the similar case of not having sugary foods). That component of the utility losses seems like it would be closer to linear. 

Gotcha, makes sense!

I think that line in the summary is misleading. The full report says (emphasis mine):

Overall, we expect a 100% reduction in sugar-sweetened beverages consumption in a single country to reduce the global disease burden of diabetes mellitus type 2 by 0.02%.

So eliminating 100% of sugary beverage consumption in all 193 countries (# of countries used in the CEA) would prevent 3.86% of diabetes cases.

Another way to look at this is that according to the report's numbers, a 100% reduction in sugary beverages would prevent 0.02%*193 = 1 in 26 cases of diabetes. But it would also make life 1/1000th worse for everyone.