But if ň=1.5, then theory will predict changes on the scale of 0.076x to 0.128x that of the GD experiments. Ie, exactly in the boundary of whether it's possible to detect an effect at all or not!

Might be misreading, on a quick skim Sam Nolan's analysis seemed pertinent but noticed you'd already commented. Sam's reply still seems useful to me, in particular the data here

 In London, it was found to be 1.5, in 20 OECD countries, it was found to be about 1.4. James Snowden assumes 1.59. 

although none of those countries are low-income so your concern re: OOD generalisation still applies.

Probably Brian Tomasik. I think about his essay On triage every once in a while:

Triage as "warm and calculating"

But isn't this focus on efficiency cold-hearted? Doesn't strict triage neglect the pain of those who don't get preferential treatment? For example, people sometimes scoff at legitimate concerns about large pecuniary expenditures on the grounds that it's insensitive to care about money when lives are at stake. In the real world, though, we can't do everything. Resources are limited, and we inevitably face choices between helping one being or another. It is precisely the sympathy that we feel for those animals left homeless by a hurricane, for instance, that so fervently motivates us to devote our money toward more efficient ways of ameliorating animal suffering. Triage is not an act of harshness; it represents the highest form of mercy and compassion.

80K to their credit have been trying to push back on single-player thinking since at least 2016 but it doesn't seem to have percolated more widely.

Stuart Buck's new post over at The Good Science Project has one of the hardest-hitting openings I've read in a while: 

Many common medical practices do not have strong evidence behind them. In 2019, a group of prominent medical researchers—including Robert Califf, the former Food and Drug Administration (FDA) Commissioner—undertook the tedious task of looking into the level of evidence behind 2,930 recommendations in guidelines issued by the American Heart Association and the American College of Cardiology. They asked one simple question: how many recommendations were supported by multiple small randomized trials or at least one large trial? The answer: 8.5%. The rest were supported by only one small trial, by observational evidence, or just by “expert opinion only.”

For infectious diseases, a team of researchers looked at 1,042 recommendations in guidelines issued by the Infectious Diseases Society of America. They found that only 9.3% were supported by strong evidence. For 57% of the recommendations, the quality of evidence was “low” or “very low.” And to make matters worse, more than half of the recommendations considered low in quality of evidence were still issued as “strong” recommendations.

In oncology, a review of 1,023 recommendations from the National Comprehensive Cancer Network found that “…only 6% of the recommendations … are based on high-level evidence”, suggesting “a huge opportunity for research to fill the knowledge gap and further improve the scientific validity of the guidelines.”

Even worse, as shown in a great book co-authored by current FDA official Vinay Prasad, there are many cases where not only is a common medical treatment lacking the evidence to support it, but also one or more randomized trials have shown that the treatment is useless or even harmful!

(I'll refrain from quoting the rest and suggest instead you check out his post)

But to build "EA" we need to engage mostly outside of EA philosophy, as you rightly say, and start building something better.

You may be interested in Siobhan's classic 2022 post Learning from non-EAs who seek to do good. 

Nice table from the paper Epic narratives of the Green Revolution in Brazil, China, and India by Lídia Cabral, Poonam Pandey, and Xiuli Xu (2022):

How do you personally deal with this difficulty? 

I personally dealt with this (in part) by referencing Jack Malde's excellent guided cause prio flowchart (this was a first draft to gauge forum receptivity). Sadly, when asked about updates, he replied that "Interest seemed to be somewhat limited."

This passage from David Roodman's essay Appeal to Me: First Trial of a “Replication Opinion” resonated:

When we draw on research, we vet it in rare depth (as does GiveWell, from which we spun off). I have sometimes spent months replicating and reanalyzing a key study—checking for bugs in the computer code, thinking about how I would run the numbers differently and how I would interpret the results. This interface between research and practice might seem like a picture of harmony, since researchers want their work to guide decision-making for the public good and decision-makers like Open Philanthropy want to receive such guidance.

Yet I have come to see how cultural misunderstandings prevail at this interface. From my side, what the academy does and what I and most of the public think it does are not the same. There are two problems. First, about half the time I reanalyze a study, I find that there are important bugs in the code, or that adding more data makes the mathematical finding go away, or that there’s a compelling alternative explanation for the results. (Caveat: most of my experience is with non-randomized studies.) Second, when I send my critical findings to the journal that peer-reviewed and published the original research, the editors usually don’t seem interested (recent exception). Seeing the ivory tower as a bastion of truth-seeking, I used to be surprised. I understand now that, because of how the academy works, in particular, because of how the individuals within academia respond to incentives beyond their control, we consumers of research are sometimes more truth-seeking than the producers.

I had a similar realisation towards the end of my studies which was a key factor in persuading me to not pursue academia. Also I've mentioned this before, but it surprised me how much more these kinds of details mattered in my experience in industry.

Skipping over to his recap of the specific case he looked into:

To recap:

• Two economists performed a quantitative analysis of a clever, novel question.
• It underwent peer review.
• It was published in one of the top journals in economics. Its data and computer code were posted online, per the journal’s policy.
• Another researcher promptly responded that the analysis contains errors (such as computing average daytime temperature with respect to Greenwich time rather than local time), and that it could have been done on a much larger data set (for 1990 to ~2019 instead of 2000–04). These changes make the headline findings go away.
• After behind-the-scenes back and forth among the disputants and editors, the journal published the comment and rejoinder.
• These new articles confused even an expert.
• An outsider (me) delved into the debate and found that it’s actually a pretty easy call.

If you score the journal on whether it successfully illuminated its readership as to the truth, then I think it is kind of 0 for 2. ...

That said, AEJ Applied did support dialogue between economists that eventually brought the truth out. In particular, by requiring public posting of data and code (an area where this journal and its siblings have been pioneers), it facilitated rapid scrutiny.

Still, it bears emphasizing: For quality assurance, the data sharing was much more valuable than the peer review. And, whether for lack of time or reluctance to take sides, the journal’s handling of the dispute obscured the truth.

My purpose in examining this example is not to call down a thunderbolt on anyone, from the Olympian heights of a funding body. It is rather to use a concrete story to illustrate the larger patterns I mentioned earlier. Despite having undergone peer review, many published studies in the social sciences and epidemiology do not withstand close scrutiny. When they are challenged, journal editors have a hard time managing the debate in a way that produces more light than heat.

I have critiqued papers about the impact of foreign aid, microcredit, foreign aid, deworming, malaria eradication, foreign aid, geomagnetic storm risk, incarceration, schooling, more schooling, broadband, foreign aid, malnutrition, …. Many of those critiques I have submitted to journals, usually only to receive polite rejections. I obviously lack objectivity. But it has struck me as strange that, in these instances, we on the outside of academia seem more concerned about getting to the truth than those on the inside. 

Here you go: https://archive.is/Q2okZ 

The part about "what if money were no object?" reminds me of Justin Sandefur's point in his essay PEPFAR and the Costs of Cost-Benefit Analysis that (emphasis mine)

Budgets aren’t fixed

Economists’ standard optimization framework is to start with a fixed budget and allocate money across competing alternatives. At a high-level, this is also how the global development community (specifically OECD donors) tends to operate: foreign aid commitments are made as a proportion of national income, entirely divorced from specific policy goals. PEPFAR started with the goal instead: Set it, persuade key players it can be done, and ask for the money to do it.

Bush didn’t think like an economist. He was apparently allergic to measuring foreign aid in terms of dollars spent. Instead, the White House would start with health targets and solve for a budget, not vice versa. “In the government, it’s usually — here is how much money we think we can find, figure out what you can do with it,” recalled Mark Dybul, a physician who helped design PEPFAR, and later went on to lead it. “We tried that the first time and they came back and said, ‘That’s not what we want...Tell us how much it will cost and we’ll figure out if we can pay for it or not, but don’t start with a cost.’” 

Economists are trained to look for trade-offs. This is good intellectual discipline. Pursuing “Investment A” means forgoing “Investment B.” But in many real-world cases, it’s not at all obvious that the realistic alternative to big new spending proposals is similar levels of big new spending on some better program. The realistic counterfactual might be nothing at all.

In retrospect, it seems clear that economists were far too quick to accept the total foreign aid budget envelope as a fixed constraint. The size of that budget, as PEPFAR would demonstrate, was very much up for debate.

When Bush pitched $15 billion over five years in his State of the Union, he noted that $10 billion would be funded by money that had not yet been promised. And indeed, 2003 marked a clear breaking point in the history of American foreign aid. In real-dollar terms, aid spending had been essentially flat for half a century at around $20 billion a year. By the end of Bush’s presidency, between PEPFAR and massive contracts for Iraq reconstruction, that number hovered around $35 billion. And it has stayed there since. (See Figure 2)

Compared to normal development spending, $15 billion may have sounded like a lot, but exactly one sentence after announcing that number in his State of the Union address, Bush pivoted to the case for invading Iraq, a war that would eventually cost America something in the region of $3 trillion — not to mention thousands of American and hundreds of thousands of Iraqi lives. Money was not a real constraint.

A broader lesson here, perhaps, is about getting counterfactuals right. In comparative cost-effectiveness analysis, the counterfactual to AIDS treatment is the best possible alternative use of that money to save lives. In practice, the actual alternative might simply be the status quo, no PEPFAR, and a 0.1% reduction in the fiscal year 2004 federal budget. Economists are often pessimistic about the prospects of big additional spending, not out of any deep knowledge of the budgeting process, but because holding that variable fixed makes analyzing the problem more tractable. In reality, there are lots of free variables.