When you say your aim is

I can very honestly, and with a lot of authority, tell a potential donor that if they donate to a particular charity, they will avert at least one DALY or save at least 1 child's life for the given cost

I think you're looking for a level of certainty that basically doesn't exist in any of the top recommendations, whether by GiveWell, GWWC, etc let alone the higher-EV lower-certainty giving opportunities (policy reform, field-building, etc), and that the kinds of adjustments you make won't get you where you want. You can and should argue that a particular giving opportunity is great based on multiple lines of reasoning, but you can't make your exact claim as phrased (apologies if the following may seem nitpicky — I used to require the same sort of certainty too, so consider it an attempt to legibilize why I changed my mind).

The reasons for this are hard to summarise in a persuasive way if you haven't built intuition by scrutinising a (good) CEA. You can get a sense of them by picking a charity everyone agrees is a long-running top recommendation for cost-effectiveness + evidence robustness + track record (proxying your search for authoritative certainty), e.g. the Against Malaria Foundation, and seeing what GiveWell says about them. So let's do that: 

• it turns out that the final cost-eff estimate depends a lot on which region you're looking at (from 4.8x cash in Chad to 22.6x in Guinea), so let's pick the DRC (14.5x) to illustrate
• GW begin with a $1M grant size as placeholder value, which in the DRC covers 94.49% of total costs with the small remainder covered by the government, yielding $1,058,303 total spending by all contributors (it's more complicated than this, see this cell's note, but the complication doesn't affect the bottomline)
• this spending leads to the distribution of 180,896 insecticide-treated nets at $5.85 per net, but GW calculate here using data from the Pryce et al 2018 meta-analysis that only about 70% of them were used in trials, and GW discounts this further by -10% in program contexts out of skepticism (trial-to-program adjustment is quite complicated, see GW's analysis here), so their best guess is only 113,965 (63%) of those 180,896 nets actually end up being used. This varies a lot by region already, e.g. in Chad (whose cost-eff being ~1/3rd of the DRC's above might make you think is due to fewer nets being used) GW actually estimates 167,299 nets used (+47% more than in the DRC)
• GW's analysis indicates that ~2.15 people sleep under each net in the DRC (this varies by region too, going down to 1.72 in Togo for instance), so those actually-used nets above cover 245,154 people. Another very complicated calculation with a plethora of best-guesses adjustments yields an estimate of ~1.2 effective coverage years provided per net (lower than in many other regions which get ~2.0 ec-years), hence 292,350 person-years of coverage
• ~16% of DRC's national population is under-5 children, so GW assumes ~16% or 45,449 of those person-years are for under-5s, for whom benefit estimation is calculated separately because child mortality rate is much higher than in older groups — so much so that GW estimates 81% of the lives saved (and 87% of the mortality reduction benefit) are from these 16% of beneficiaries. As an aside, given this context you'll be glad to hear that GW estimates 59.1% of under-5 children sleep under nets in the DRC (varying from 31-80% in the regions AMF operates that GW analysed) using data from the Malaria Atlas Project combined with GW's ITN coverage estimates
• GW estimates a 56% expected reduction in malaria incidence among children who sleep under nets in the DRC. This comes from Pryce et al 2018's 64% implied reduction with 3 further discounts: -5% for internal validity ("to account for the possibility that the summary effect reported in Lengeler's 2004 meta-analysis might not have been the true effect ITNs had on the populations being studied"), -5% for external validity (to account for differences between trials and program implementation — see this Asterisk essay for a particularly stark example of how different scale-up results can be, and note that GW still assign a discount out of general skepticism even after separately discounting for their 3 major external validity concerns of net usage, country-specific mortality rates, and insecticide resistance), and -4% for insecticide resistance (in yet another complicated calculation that shows how region-dependent net effectiveness reduction due to resistance is, going as high as -59% in Chad which partly explains its low cost-eff)
• GW estimates that in the absence of nets, the total under-5 mortality rate attributable to malaria alone would be 0.80%. This is full of adjustments too: GW start with the fact that in the DRC 0.31% of under-5 deaths in 2019 were directly attributable to malaria (40,985 deaths among 13.4M children), adjust this up +75% to 0.53% to account for indirect malaria deaths (GW's complicated reasoning concludes "we're highly uncertain about the exact value of this input", but it's more skeptical than the best literature implies and is roughly the midpoint of expert opinion), adjusts for deaths averted due to seasonal malaria chemoprevention or SMC programs (delivered by another top GW charity) to avoid double-counting, and finally adjusts for the 59.1% net coverage in the DRC mentioned earlier
• combining everything together: $1M GW grant → $1,058,303 total spending including DRC government funds mobilised → 180,896 nets distributed → 113,965 nets used → 245,154 people covered → 292,350 person-years of effective coverage → 45,449 person-years of under-5 coverage → 203 under-5 deaths averted. A similarly adjustment-filled calculation yields 48 deaths averted among older people, so 251 total deaths averted (this is only halfway through GW's main CEA calc but the rest won't be germane to the point)
• Naively we might say based on this that a $3,989 donation saves one life in the DRC, and that this varies from $1,700 in Guinea to $14,493 in Chad, but after seeing the kinds of adjustments that GW had to make above (do dig into those links to get a better sense of what I mean), I'm not sure you would be comfortable telling prospective donors that (say) giving $15,000 to AMF would save "at least one child's life". All you can say is that it would do so in expectation, and that the perspective shift is worth making as there are good reasons for giving (and doing good) guided by an expected value lens, while also being aware of this lens' shortcomings 

Notice how difficult it is to get the level of certainty you seek in every adjustment step of the calculation above, despite GW's almost pathologically-skeptical thoroughness. This doesn't mean AMF is "a bad recommendation" — it means virtually every other top giving opportunity is "worse" (by the standard you seek) because giving well is really hard, yet we still have very good reasons to think that AMF and all those other top recommendations are excellent giving opportunities, so you should recalibrate your expectations and communicate this to potential donors as well who seek the same.  

re: the "multiple lines of reasoning" alternative for recommending great giving opportunities, GiveWell's approach is solid I think. For AMF that looks like

We recommend AMF because of its:

• Implementation of a program (mass distribution of ITNs) that we think is very cost-effective (more in our separate report on ITNs).
• Long track record of supporting campaigns in a number of countries. (More)
• Processes for tracking other actors’ malaria spending in the countries where AMF works, giving us more confidence that it is filling genuine funding gaps. (More)
• Transparency – AMF shares significant information about its work with us. (More)

and GW detail their reservations as well (same link).