MacKenzie Scott's $26 billion, in QALYs: an interactive, evidence-weighted CEA

By Max Ghenis @ 2026-07-12T19:45 (+11)

Epistemic status: a fully parameterized Monte Carlo model whose every input is inspectable and overridable — not a measured fact. The cost-effectiveness anchors come from published causal studies; the judgment priors (evidence-credibility tiers, realization, allocation concentration) were drafted by Claude and reviewed by me, and each is a slider. I'd value attacks on specific parameters over general takes.

MacKenzie Scott has given away about $26 billion since 2019 — roughly a third of all US megagifts in 2025. Almost none of it is denominated in health. I built an interactive model that asks what it bought in QALYs anyway:

https://maxghenis.com/mackenzie-scott-qaly

Headline at the skeptical default (100k draws): median ~70,000 QALYs (90% interval 38k–136k), a blended ~$435k per QALY, benefit/cost 1.7× at HHS's value per QALY. Take every cited effect at face value instead and the median is ~200,000. The gap between those two numbers — the causal-credibility discount — is the finding.

Against the global-health frontier

Her blended portfolio prices out at ~$435,000 per QALY. GiveWell's current program averages (~$4,000–5,500 per under-5 life saved, 2022–24) convert to ~$200 per discounted QALY under this model's own conventions — ~25 discounted QALYs per child death at 3%, which is stricter than the undiscounted ~$100/DALY folk numbers. Handicapped with the same realization and credibility discounts, the frontier still buys ~1,500× more health per marginal dollar.

That's a marginal comparison. How far does the frontier fall as money scales? Three points on the supply curve, in GiveWell's own units (multiples of their cash benchmark):

• Today's best and average. GiveWell's most recent Helen Keller grant is ~25× their benchmark; across all 2025 grants the bar was ≥8× and the dollar-weighted average ~16×, on $418M granted. Scott's recent ~$5B/year pace is roughly twelve GiveWells.
• The revealed curve. When funding grew in 2021–22, GiveWell planned to fund up to ~$750M/yr of opportunities at ≥6× the benchmark; when projections fell they raised the bar back to 10×. Billions per year plausibly clears in the mid single digits of the benchmark — a few hundred times her blended portfolio in health terms.
• The floor moved up. The benchmark itself is cash to the very poor — the intervention with the most absorptive capacity — and GiveWell now estimates GiveDirectly at 3–4× its own historic benchmark, on recipient spillovers and new child-mortality results (my replication scores it at ~2.6–3.8× across countries). Cash now beats "1× cash"; GiveWell kept the historic unit while it re-evaluates.

In this model's health-only currency, the child-mortality channel alone prices cash at roughly $8,000 per discounted QALY (GiveWell's uncertainty-adjusted ~23% under-5 reduction ≈ $200,000 per child death averted) — ~50–100× more health per dollar than her blended portfolio, at effectively unbounded scale. And that's conservative: Richterman et al. (2023, Nature) find government cash programs across 37 countries associated with a ~20% reduction in adult-female mortality (and 8% under-5, with population-wide spillovers), and the GiveDirectly RCT itself measured a +0.26 SD gain in psychological well-being — a quality-of-life channel, not mortality. Cash's pure consumption value stays out of frame here (as does Scott's non-health value), but its measured health effects belong in, and they push the floor multiple below 50–100×. Money isn't the only binding input either: even funded, evidence-backed interventions hit delivery-capacity limits. So: ~1,500× at today's margin, plausibly a few hundred× for marquee programs at her scale, and somewhere under ~50–100× even if every dollar became cash.

What's different from a standard back-of-envelope

The allocation is her own data, not a guess. Yield Giving's gift database discloses dollar amounts for 2,035 of 2,711 gifts (~two-thirds of the total), with org-reported focus areas on every disclosed dollar. Each gift's dollars split across its organization's areas, mapped onto 13 intervention archetypes (every mapping rule documented). The undisclosed third is imputed, not dropped: her essays give each year's total, so the residual is a known dollar amount, distributed over that year's undisclosed gifts in proportion to the recipient's pre-gift IRS 990 revenue (via ProPublica's Nonprofit Explorer) raised to an elasticity fit on the disclosed pairs. Imputation moves each cause share by at most ~1.5pp — the disclosed two-thirds was representative.

Cost-per-QALY comes from causal estimates where they exist. Medicaid mortality (Sommers 2017; Miller, Johnson & Wherry 2021), community health centers (Bailey & Goodman-Bacon 2015), supportive housing (Holtgrave et al. 2013), collaborative-care depression. Where no health pathway has credible evidence (equity & justice, civic, arts — the largest dollar buckets), the model uses wide skeptical priors rather than an optimistic sector average.

Every effect is shrunk by how credibly it's identified. Each archetype's evidence gets a design tier (randomized/lottery → strong quasi-experimental → … → assumption-only), and a credibility weight drawn from that tier's Beta distribution linearly shrinks the effect toward zero health impact. Internal validity only; transport and delivery live in a separate realization factor, so the layers don't double-count. The tier levels are AI-proposed, author-reviewed priors — the ordering is the defensible part, and the evidence-stance slider sweeps them from skeptical to face value.

A byproduct finding

Across 1,313 disclosed gift–revenue pairs, gift size scales with the recipient's pre-gift revenue to the power 0.41 (R² 0.37). A 10× bigger organization gets about 2.5× more money — her giving is far flatter across organization size than proportional.

What this doesn't capture

A QALY is a health metric. Most of Scott's giving targets economic mobility, education, and equity, whose value is largely non-health; a WELLBY or consumption frame would credit those buckets far more. The model is deliberately scoped to one question: how much health does the money buy? It is not a verdict on her choices.

Process, for those interested in AI-assisted research

The accounting is mechanical; the judgment layer is where the AI worked. Claude drafted the credibility tiers and priors; Codex (GPT-5.6-sol) reviewed the assumptions cold across several adversarial rounds and caught real errors (an un-inflated 2007-dollar figure, a $/life-year used as $/QALY, a misattributed citation); GPT-5.6-terra audited 827 nonprofit name→EIN matches against the live IRS data. Each correction made the model more skeptical or more honest, and none was caught by a human. Every prompt I typed is in the blog appendix, verbatim.

Model, tests, parameter file with inline citations: github.com/MaxGhenis/mackenzie-scott-qaly. It shares machinery with the interactive replication of GiveWell's cost-effectiveness model I posted here earlier this year (tool) — this one points the same lens at a single donor's actual portfolio instead of the top charities.

If you think a specific parameter is wrong, the parameter file cites its source inline, and I'll take PRs — or just move the slider and see whether it matters.