I wonder how they select grants to showcase on that page. They've made grants that are both much larger and more cost-effective than that, e.g. this $71.5M grant in Jan '23 to HKI's vitamin A supplementation program that they estimate would save roughly 49,000 lives at ~$1,450 per life saved after all adjustments (or ~93,000 lives at $770 per life if only adjusting for internal and external validity, or nearly 280k lives at at $260 per life saved before any adjustments, i.e. the standard I usually see in most BOTECs claiming to "beat GW top charities"...). Only thing is, this wouldn't be obvious from their original CEA because they tend to input "donation (arbitrary size)" = $100k instead of the actual grant amounts; I had to manually input their grant budget breakdown into a copy of their CEA to get the numbers above (which also means I may have done it wrong, so caveat utilitor...)

RP's CEA of LN is done by quarter, so it's quarterly. More pertinently to the part you quoted, row 144 is saying the income of the women reached for counselling is modelled as increasing by $1 for every $1.17 of all-in program cost in Q1 '24, the latter quickly dropping to just $0.25 by Q4 '25. If you're wondering what this corresponds to in terms of % increase in earnings, it's 19%, from Canning and Schultz (2019) with -20% and -40% discounts for internal and external validity respectively.

I'm admittedly confused by this. I suppose when you wrote

... none of these have two ticks in my estimation. However, combined, I think this list represents a threat that is extremely likely to be real and capable of ending a galactic civilisation.

you meant that, combined, they nudge your needle 10%?

I've read those comments awhile back and I don't think they support your view for relying overwhelmingly on explicit quantitative cost-effectiveness analyses. In particular the key parts I got out of Isabel's comment weren't what you quoted but instead (emphasis mine not hers)

Cost-effectiveness is the primary driver of our grantmaking decisions. But, “overall estimated cost-effectiveness of a grant” isn't the same thing as “output of cost-effectiveness analysis spreadsheet.” (This blog post is old and not entirely reflective of our current approach, but it covers a similar topic.)

and 

That is, we don’t solely rely on our spreadsheet-based analysis of cost-effectiveness when making grants. 

which is in direct contradistinction to your style as I understand it, and aligned with what Holden wrote earlier in that link you quoted (emphasis his this time)

While some people feel that GiveWell puts too much emphasis on the measurable and quantifiable, there are others who go further than we do in quantification, and justify their giving (or other) decisions based on fully explicit expected-value formulas. The latter group tends to critique us – or at least disagree with us – based on our preference for strong evidence over high apparent “expected value,” and based on the heavy role of non-formalized intuition in our decisionmaking. This post is directed at the latter group.

We believe that people in this group are often making a fundamental mistake, one that we have long had intuitive objections to but have recently developed a more formal (though still fairly rough) critique of. The mistake (we believe) is estimating the “expected value” of a donation (or other action) based solely on a fully explicit, quantified formula, many of whose inputs are guesses or very rough estimates. We believe that any estimate along these lines needs to be adjusted using a “Bayesian prior”; that this adjustment can rarely be made (reasonably) using an explicit, formal calculation; and that most attempts to do the latter, even when they seem to be making very conservative downward adjustments to the expected value of an opportunity, are not making nearly large enough downward adjustments to be consistent with the proper Bayesian approach.

This view of ours illustrates why – while we seek to ground our recommendations in relevant facts, calculations and quantifications to the extent possible – every recommendation we make incorporates many different forms of evidence and involves a strong dose of intuition. And we generally prefer to give where we have strong evidence that donations can do a lot of good rather than where we have weak evidence that donations can do far more good – a preference that I believe is inconsistent with the approach of giving based on explicit expected-value formulas (at least those that (a) have significant room for error (b) do not incorporate Bayesian adjustments, which are very rare in these analyses and very difficult to do both formally and reasonably).

Note that I'm not saying CEAs don't matter, or that CEA-focused approaches are unreliable — I'm a big believer in the measurability of things people often claim can't be measured, I think in principle EV-maxing is almost always correct but in practice it can be perilous and on the margin people should instead be working a bit more on how different moral conceptions cash out in different recommendations more systematically e.g. with RP's work, if a CEA-based case can't be made for a grant I get very skeptical, I in fact also consider CEAs the main input into my thinking on these kinds of things, etc. I am simply wary of single-parameter optimisation taken to the limit in general (for anything, really, not just for donating), and I see your approach as being willing to go much further along that path than I do (and I'm already further along that path than almost anyone I meet IRL). 

But I've seen enough back-and-forth between people in the cluster and sequence camps to have the sense that nobody really ends up changing their mind substantively and I doubt this will happen here either, sorry, so I will respectfully bow out of the conversation.

Actually now that you mention the shifting health budget allocation I'm also skeptical, although I'm mostly thinking of CEAP's experience finding the development budget ~fixed and the remaining sliver fought over by hundreds of NGOs, I take you to be saying it's the same story for health budget.

I agree re: impact attribution determination and they don't seem to be planning to do that in their plans for follow-up section. 

GW's email newsletter just alerted me to their grant writeup on this, in case you still want to look into it / aren't subbed to them :) especially the main reservations section

Yeah agreed, that's why I mentioned the cluster thing as a point in favor of your report's analytical methodology, and brought up the VC vs index fund analogy, essentially to steer the focus away from pure EV-maxing

Yeah, higher in fact (the model's cost-eff estimate seems to be out of date vs the report summary's as it's lower DALYs averted per $100k). Keep in mind that policy advocacy is extremely hits-based (lots of nothingburgers per win, but the wins can be so big for the right problem/intervention combos you can still come out way ahead) compared to say nets or antimalarial drugs, a bit like VCs vs index funds in risk/return profile, so instead of looking only at EV, which is a fragile sequence thinking approach, you take a cluster thinking approach (which is better for making good decisions) and also consider things like the underlying theory of change's evidence quality (high in this case), expert views (checks out), and downsides (marginal), all of which the author analyses extensively. 

Thanks for clarifying, seems similar to 80K's view.

Out of curiosity

I am very much not a utilitarian (though I think consequences are very important)

Using my moral intuition, the case against utilitarianism (and consequentialism) seems very strong

I'm wondering how to square these statements re: your attitude towards consequentialism (not utilitarianism). I suppose you're saying you think consequences are very important yet you aren't a consequentialist in the way most people who call themselves that use/define the term?