Hi all,
Thank you, Allegra for the well presented initial post, and for constructive replies. I thought I would share how I’ve been grappling with this standard EA dilemma regarding donations to acute humanitarian crises (as we see now in Sudan). The default position is that while morally urgent, these donations may be 1-2 orders of magnitude less cost-effective (in $ per life saved) than top GiveWell picks. This often leads to a “head vs. heart” framework, where one might allocate a 10-20% “moral” portion to crisis relief.
However, in thinking this through, I believe this binary view misses several distinct, high-impact frameworks that are defensible from an EA perspective.
1. The Middle Ground: Anticipatory Action (AA)
The first and most obvious “fix” is to reframe disaster aid as proactive rather than reactive. This is the “Anticipatory Action” (or Early Warning, Early Action) model.
This framework applies most significantly to predictable shocks (floods, droughts) and represents investment rather than mere relief. The data on its cost-effectiveness is compelling:
High ROI: Multiple agencies, including the UN’s Food and Agriculture Organization (FAO) and the World Food Programme (WFP), have demonstrated that every $1 invested in anticipatory action can save over $7 in avoided losses and added benefits for beneficiaries (e.g., protecting assets, avoiding debt, and reducing the need for costly emergency food aid) [1, 2].
A Proven Case (Somalia 2011 vs. 2017): This is a powerful A/B test. In 2011, a delayed, reactive response to famine warnings saw over 260,000 deaths. In 2017, similar drought forecasts were met with a large-scale early response, which successfully averted a full-blown famine [3].
For predictable crises, AA seems to be a high-CE, evidence-based intervention that fits comfortably within EA frameworks.
1. The “Tourniquet” Framework for Protracted Crises
This is the point I’m most focused on, as it applies to complex, ongoing crises like Sudan, where the “anticipation” window may seem to have passed.
My concern is that even in a full-blown crisis, things can always get worse. The crisis “cascades.” An initial conflict (a security crisis) triggers a displacement crisis, which triggers a health system collapse, which triggers a cholera epidemic and a food security crisis (IPC Phase 4).
The “tourniquet” framework argues that humanitarian aid in this context, while reactive to the initial shock, is critically preventative of the next, worse cascade.
This is not a hypothetical. This is what is happening in Sudan right now:
Preventing Mass Famine: As of September 2024, the Integrated Food Security Phase Classification (IPC) has confirmed that Famine (IPC Phase 5) is already occurring in towns like El Fasher and Kadugli. Over 21 million people are in Crisis (IPC Phase 3+) [4]. Aid from the WFP is not just “reacting” to hunger; it may be the only tourniquet preventing the 375,000+ people currently in Catastrophe (IPC 5) and the millions in Emergency (IPC 4) from cascading into mass starvation.
Preventing Epidemic Collapse: As of late 2024, Sudan is facing one of the world’s worst cholera outbreaks, with over 120,000 suspected cases [5]. With 70-80% of health facilities non-functional, UNICEF and WHO’s work to provide oral rehydration salts and clean water is not just “reacting” to cholera; it is applying the tourniquet to prevent an uncontrolled epidemic from killing tens of thousands.
I recognize the cost-effectiveness evidence here is less precise than for GiveWell interventions. But in crisis contexts, waiting for perfect data is itself a choice with costs. The cascade logic—preventing famine from becoming mass starvation, preventing cholera from becoming uncontrolled epidemic—is structurally high-impact even when exact figures are elusive. Demanding Randomized Controlled Trial level evidence for crisis response is effectively a decision to not act, which itself has an implicit CE assumption baked in.
1. The Portfolio Risk-Return Framework
It may be worth thinking about crisis giving in terms of portfolio theory. Different interventions have different risk-return profiles:
GiveWell charities: Steady, reliable impact with strong evidence (high certainty, proven returns)
Anticipatory Action: Strong expected value with good evidence (7:1 ROI, growing evidence base)
Crisis “tourniquet”: Higher variance, but with significant upside potential and bounded downside
In crisis response, the downside is bounded—even if Sudan aid is “only” as cost-effective as a standard charity, you’ve still saved lives. But the upside is significant: if you successfully prevent cascade collapse (famine to epidemic to state failure), you may have 10-100x impact. Focusing on reputable organizations (MSF, IRC, UNICEF) with operational excellence increases the probability of realizing those high returns.
A sophisticated giving portfolio can include high-certainty interventions alongside some allocation to higher-variance, higher-upside opportunities. This isn’t abandoning EA principles; it’s applying them with more nuance.
1. Portfolio Sustainability and Donor Engagement
Finally, there’s a meta-consideration: donor sustainability. Allocating 10-20% to emotionally engaging, tangible crises—even if the CE evidence is less certain—may sustain long-term commitment to effective giving. If this prevents burnout and maintains 80% highly effective giving over decades rather than years, the expected value is substantial.
This isn’t “giving in” to emotion; it’s recognizing that sustained impact requires sustainable practice. Humans are the optimization engine in EA, and maintaining that engine matters too. Personal connection to tangible crises may also help prevent the abstract utilitarian failure modes that can come from treating all suffering as mere numbers.
A Revised Framework
This suggests a more nuanced “moral portfolio” for donations:
80% Core (GiveWell): Maximizing CE on chronic, tractable problems
10% High-Impact Crisis (AA): High-CE prevention for acute, predictable problems
10% Urgent “Tourniquet” (Acute Response): High-leverage prevention for ongoing, cascading problems
Portfolio Sustainability: Maintaining long-term donor engagement and impact
From this perspective, donating to a high-quality organization (like MSF, UNICEF, or the IRC) in Sudan isn’t just a low-CE “heart” donation. It’s a defensible allocation that combines cascade prevention logic, portfolio diversification, and long-term sustainability considerations.
EA’s strength is rigorous thinking about impact. But rigor shouldn’t mean rigidity. A sophisticated giving approach can include all of these elements while remaining committed to doing the most good possible.
Sources
[1] FAO (Food and Agriculture Organization). (2023). “Impact of disasters on agriculture and food 2023.” Cites cost-benefit ratios of up to 7.1 (i.e., $1 saves $7.10) for anticipatory action.
<https://www.fao.org/3/cc7900en/online/impact-of-disasters-on-agriculture-and-food-2023/anticipatory-action-interventions.html>
[2] WFP (World Food Programme). (2025). “COP30: How Anticipatory Action helps people prepare.” Notes their first large-scale AA rollout was “at half the cost” of a traditional response.
<https://www.wfp.org/stories/cop30-how-anticipatory-action-helps-people-prepare-extreme-weather-strikes>
[3] Refugees International. (2022). “We Were Warned: Unlearned Lessons of Famine in the Horn of Africa.” Details how the “hard lessons” from the 2011 famine failure were applied to successfully avert famine in 2017-2018.
<https://www.refugeesinternational.org/reports-briefs/we-were-warned-unlearned-lessons-of-famine-in-the-horn-of-africa/>
[4] IPC (Integrated Food Security Phase Classification). (September 2024). “Sudan: Famine confirmed in El Fasher and Kadugli towns.” The official analysis confirming Famine (IPC Phase 5) and detailing that 21.2 million people faced high acute food insecurity (IPC 3+) as of September 2024.
<https://www.ipcinfo.org/ipcinfo-website/countries-in-focus-archive/issue-137/en/>
[5] Wikipedia / Health Authorities. (October 2024). “2024–2025 Sudanese cholera epidemic.” Synthesizes WHO, UNICEF, and Ministry of Health reports, noting over 120,496 cases and 3,368 deaths recorded by mid-October 2024.
<https://en.wikipedia.org/wiki/2024%E2%80%932025_Sudanese_cholera_epidemic>
That’s an excellent point, and you’re absolutely right to question that “1-2 OOM” heuristic. I agree with you, and I think your insight gets to the core flaw in that “default” assumption (which I was also challenging).
That heuristic seems to originate from a (now quite old) 2010 GiveWell blog post, “Can choosing the right charity double your impact?” [1]. In it, they made the case that the total range of charity effectiveness “can easily vary by 2-3 orders of magnitude” and guessed that “disaster relief funds are closer to the less-cost-effective end of the range.”
This appears to have hardened over time into a “default position.” This isn’t just because of that old post, but because GiveWell’s current research still reinforces this gap. Their top-tier picks are consistently modeled at ~$4,000-$5,500 per life saved [2], while their current minimum bar for new programs is “10x cash” (i.e., one order of magnitude) [3]. To my knowledge disaster response has never been recommended; their current page on the topic still states it “may not be the ideal cause,” effectively placing the entire category (by default) below the 10x cash threshold [4].
But as you’ve correctly pointed out, this heuristic is flawed because it lumps all disaster relief into one category.
I agree that even basic cash transfers targeted to Sudan’s crisis-affected populations are likely much more cost-effective than the old heuristic suggests—your GiveDirectly comparison is well-taken. But my argument focuses on irreplaceable service delivery like MSF, where the calculation may be even more stark. In a total system collapse, the CE calculation changes. The “tourniquet” value of an org like MSF is not “Cost of MSF” vs. “Cost of Malaria Net.” It’s “Cost of MSF” vs. “very high preventable mortality” for that specific cohort (e.g., cholera patients, surgical trauma victims) because the service is the only thing preventing the next cascade.
So, I fully agree. The 1-2 OOM gap is a flawed heuristic to apply here, and it’s highly plausible that this specific, targeted “tourniquet” intervention is dramatically more cost-effective than the old model suggests. The primary cost-effectiveness gap that remains is likely one of operational delivery in a warzone, not a lack of impact-per-dollar received by the beneficiary. The data would tell more of the story, but accessing high-quality data in a crisis is hugely challenging.
Thanks for adding that critical distinction. It’s a more accurate way to frame the problem.
[1] GiveWell. (2010). “Can choosing the right charity double your impact?” https://blog.givewell.org/2010/01/28/can-choosing-the-right-charity-double-your-impact/
[2] GiveWell. (2025). “Our Top Charities.” https://www.givewell.org/charities/top-charities
[3] GiveWell. (2024). “GiveWell’s Cost-Effectiveness Analyses.” https://www.givewell.org/how-we-work/our-criteria/cost-effectiveness/cost-effectiveness-models
[4] GiveWell. “Disaster relief charities.” https://www.givewell.org/international/disaster-relief