I’m curating this post. Karthik Tadepalli makes the point that EAs have often accepted the argument, given here, that the most cost effective global health interventions are likely to be aimed at increasing growth in LMICs rather than directly targeted at health outcomes. However, there hasn’t yet been a focus on producing growth interventions within EA global health work that is proportional to this interest. In a careful, well evidenced manner, this post outlines some factors which affect economic growth in LMICs, and which may be amenable to interventions. You can read more about the discussion of boosting economic growth as a potentially tractable cause in Global Health and Wellbeing on this topics tag, and in this recent 80k podcast episode with GiveWell’s co-founder Elie Hassenfeld.  I hope, with Karthik, that this post and the series to follow is read with “an entrepreneurial eye”, and reignites debate in this pressing question. 

[Meta] Forum bug: when there were no comments it was showing as -1 comments

I'm glad you found my comment useful. I think then, with respect, you should consider retracting some of your previous comments, or at least reframing them to be more circumspect and be clear you're taking issue with a particular framing/subset of the AIXR community as opposed to EA as a whole. As for the points in your comment, there's a lot of good stuff here. I think a post about the NRRC, or even an insider's view into how the US administration thinks about and handles Nuclear Risk, would be really useful content on the Forum, and also incredibly interesting! Similarly, I think how a community handles making 'right-tail recommendations' when those recommendations may erode its collective and institutional legitimacy[1] would be really valuable. (Not saying that you should write these posts, they're just examples off the top of my head. In general I think you have a professional perspective a lot of EAs could benefit from) I think one thing where we agree is that there's a need to ask and answer a lot more questions, some of which you mention here (beyond 'is AIXR valid'): * What policy options do we have to counteract AIXR if true? * How do the effectiveness of these policy options change as we change our estimation of the risk? * What is the median view in the AIXR/broader EA/broader AI communities on risk? And so on. 1. ^ Some people in EA might write this off as 'optics', but I think that's wrong

(@Burner1989 @David Rhys Bernard @Karthik Tadepalli) I think the fundamental point (i.e. "You cannot use the distribution for the expected value of an average therapy treatment as the prior distribution for a SPECIFIC therapy treatment, as there will be a large amount of variation between possible therapy treatments that is missed when doing this.") is on the right lines, although subsequent discussion of fixed/random effect models might confuse the issue. (Cf. my reply to Jason). The typical output of a meta-analysis is an (~) average effect size estimate (the diamond at the bottom of the forest plot, etc.) The confidence interval given for that is (very roughly)[1] the interval we predict the true average effect likely lies. So for the basic model given in Section 4 of the report, the average effect size is 0.64, 95% CI (0.54 - 0.74). So (again, roughly) our best guess of the 'true' average effect size of psychotherapy in LMICs from our data is 0.64, and we're 95% sure(*) this average is somewhere between (0.54, 0.74). Clearly, it is not the case that if we draw another study from the same population, we should be 95% confident(*) the effect size of this new data point will lie between 0.54 to 0.74. This would not be true even in the unicorn case there's no between study heterogeneity (e.g. all the studies are measuring the same effect modulo sampling variance), and even less so when this is marked, as here. To answer that question, what you want is a prediction interval.[2] This interval is always wider, and almost always significantly so, than the confidence interval for the average effect: in the same analysis with the 0.54-0.74 confidence interval, the prediction interval was -0.27 to 1.55. Although the full model HLI uses in constructing informed priors is different from that presented in S4 (e.g. it includes a bunch of moderators), they appear to be constructed with monte carlo on the confidence intervals for the average, not the prediction interval for

Thanks Rebecca. I will delete the duplicates.