On behalf of all fools, I really appreciate the "April Fools' Day" tag.

I was wondering this too. If you haven't found out already, submit one per form (after submitting you will be asked if you want to submit another).

Money begets money

Or, and this is a bit more of a slogan:

If you want your money invested wisely, find the person who needs it the most.

I am not working on MAP. That is a  project mostly funded by the Bill & Melinda Gates Foundation. That post I made a week ago was just intended as a potentially-interesting description, however as I mention there MAP estimates drive both WHO and GBD estimates. I was also surprised to only recently find out about MAP given that role, and their own slick site.

I have acquired some general knowledge about malaria doing volunteer research for SoGive (to whom I am grateful). Outside of that, I am now reading up on the An. stephensi threat to Africa, but I would stop short of calling that a project. If you have a malaria-related question you want answers to that doesn't involve advanced math, there is a reasonable chance I can help.

Strong upvote for the analysis. And a more general congratulations on what you are doing with ODH (even if it doesn't turn out as effective as hoped).

I agree with the biggest weakness identified.

A quick thought (i.e. excuse me if this is stupid) is that you could use the Malaria Atlas Project (MAP) pixel data: 

to estimate burden at the location of ODH centres and analyse based on expectation of shifting mortality rate* closer to other regions where there are closer health care centers. 

A few notes:

• MAP does incorporate proximity to health care center in their modelling but I do not know how to download in a format I can use (only see TFF option).
• Proximity to health care obviously correlated with urbanisation, which reduces the malaria burden itself, but you can probably largely control for this off incidence or prevalence rates.
• Obviously less helpful approach to non-malaria burden, but I think estimations of improvement could be generalised across other health issues.
• This modelling (or at least a prior form) is a core contributor to the GBD data.

If you think there is possible merit here, I am happy to discuss.

*can be converted to DALYs, although this might be an issue if we are to consider "lives saved" in the same way as GiveWell (i.e. not using DALYs).  I have not quite got my head around if/how this is an issue yet.

Overall, I thought they were well-presented criticisms.

One exception is @34:18 in the video where Abigail quotes Anthony Kalulu that there is "no chance" someone in abject poverty will mention an EA-supported charity "as having impacted their lives more than the work of traditional global antipoverty agencies."

I saw that piece has been discussed on the forum before. My reaction to the statement was the same as the comment made by timunderwood to that forum post (i.e. it's unlikely they would due to EA-charities having a relatively small budget). And also Sanjay regarding general sceptism to the claim (and others in that piece). I didn't read all comments so others surely shared similar doubts.

My point here though is just that I thought it, and that EA's leaders "don't really want to talk about that," was a particularly poor criticism on Abigail's part and a let-down on an otherwise good critique/viewpoint on EA.

Thank you for the research.

I do not necessarily disagree with the limitation that the moral argument watered down the demandingness. However, my intuition was that the framing of the "strong demandingness" condition was particularly likely to result in the expected backfire effect.

I think real-life  strongly-demanding arguments are much more likely to use the collective "we", acknowledge that this is not socially normal, and acknowledge the incongruence of this obligation with intuitions and psychology. I don't think having this more forgiving/nuanced framing shifts it into a weaker demandingness . I appreciate that the study is also likely better as it is, but I think this is a limitation. 

This was a really neat write up.  As someone trying to do research myself (without much experience) I found it useful and I might take you up on the offer to help one day.

My intuition is that addressing the harms themselves is a more effective approach (i.e. violence and ceased education), but it is  just an intuition.

There is a typo here: "The adjusted risk ratio was 0.09 (81% decreased chance)..." should be 91% - an effect size that would be amazing if true, but does seem unlikely unfortunately.

Thanks HLI. I really like the post.

Pointing out an issue with the links to sheets that are referenced. Remove everything after "/edit" to make them work (as per below), and the latter one regardless is not publicly accessible:

https://docs.google.com/spreadsheets/d/1NMAU-a1X4vqjodjI6kf8KnUyCJaK9uyNvXWj5VetDZw

https://docs.google.com/spreadsheets/d/1RrBuiPVgL-t8hlr6EqkqABiaqdHMGkpvfeiqiiX49LU

---

Regarding the content,  as explained in Joel's comment above the immediate expected replacement effects are not included, and if they were to be you need to ask why stop after the first generation. Is there a legitimate argument however to count the first generation and stop there? Because:

• first generation replacement effect is relevant immediately, or at least within the next few years. Second generation is relevant in ~20 years, when the state of the world is much less predictable. Hopefully subjective wellbeing of people in these regions will be noticeably better in 20 years, and any replacement effect/rate might also be noticeably different.
• it is similar in immediacy and measurability (I think) to developmental, morbidity and grievance impacts that are included.

Thanks.