Replicating GiveWell's cost-effectiveness analysis in code: an interactive calculator and what I learned

I re-implemented GiveWell’s cost-effectiveness models for all six top charities as an open-source web tool: maxghenis.com/givewell-cea

This post describes the replication, what I learned about GiveWell’s methodology in the process, the verification results, and some observations about moral weight sensitivity. The tool itself lets you edit any parameter and immediately see the effect on charity rankings.

Motivation

Others have done excellent work examining specific parts of GiveWell’s CEA — Froolow’s critical review of model architecture, Nolan, Rokebrand, and Rao’s uncertainty quantification, and several pieces on deworming and AMF uncertainty. But I couldn’t find a tool that implements all six charities together and makes it easy to compare them while adjusting assumptions.

GiveWell’s spreadsheets are powerful but hard to explore casually. Each charity has its own multi-tab workbook with dozens of sheets, specialized terminology, and cross-references between cells:

Changing a moral weight means editing cells across multiple sheets and comparing results manually. I wanted something where you could adjust one slider and immediately see how all six charities re-rank.

What the model covers

For each charity I implemented the core pipeline from GiveWell’s spreadsheets:

1. People reached: Grant size / cost per person reached
2. Deaths averted (or equivalent): People reached × mortality/disease rate × intervention effect size
3. Units of value: Deaths averted × moral weight (age-adjusted)
4. Cost-effectiveness: Units of value per dollar / benchmark value per dollar
5. Adjustments: Charity-level (quality, track record), intervention-level (external validity), leverage and funging

The six charities each have their own structure:

• AMF and Malaria Consortium: Under-5 mortality reduction, with separate pathways for older age mortality and developmental effects
• Helen Keller International: VAS effect on under-5 mortality
• New Incentives: Incremental vaccinations from cash incentives, converted to deaths averted using vaccine-specific effect sizes
• GiveDirectly: Consumption increase valued directly (no mortality pathway)
• Deworm the World: Long-run earnings effects of deworming, valued via ln(consumption)

All 46 charity/country combinations operate independently — each country has its own cost per person, mortality rate, adjustment factors, etc.

Observations from the replication

A few things stood out:

The benchmark matters more than you’d think. AMF uses a benchmark of 0.00333 units of value per dollar, while MC and HKI use 0.0033545. This 0.75% difference in the denominator propagates directly to the final multiple. It took careful cell-tracing to realize different charities use slightly different benchmark constants — they’re buried in separate sheets with different update cycles.

Mortality rate definitions vary. AMF’s spreadsheet has both a raw malaria mortality rate and a derived “mortality rate in the absence of nets” rate. The latter accounts for existing net coverage and is the correct input. My first extraction accidentally used the raw rates, which underestimated AMF’s cost-effectiveness by roughly 2x for some countries (e.g., DRC: 0.00306 raw vs. 0.00798 in-absence-of-nets).

Counterfactual coverage drives most of the within-charity variation. Both Helen Keller and New Incentives have a proportionReachedCounterfactual parameter — what fraction of people would receive the intervention anyway, without the charity’s involvement. The remaining fraction is the charity’s incremental impact. These tables show how this parameter (alongside cost, mortality rates, and adjustments) determines each location’s cost-effectiveness:

Helen Keller (VAS):

Country	Existing coverage	Incremental	x benchmark
Niger	15%	85%	79×
DRC	20%	80%	30×
Mali	21%	79%	17×
Madagascar	33%	67%	12×
Guinea	18%	82%	11×
Cameroon	23%	78%	8×
Burkina Faso	29%	71%	7×
Côte d’Ivoire	40%	60%	6×

New Incentives (vaccinations, Nigerian states):

State	Would get vaccinated anyway	Incremental	x benchmark
Sokoto	69%	32%	39×
Zamfara	79%	21%	31×
Kebbi	72%	28%	29×
Bauchi	81%	19%	20×
Jigawa	86%	14%	18×
Katsina	81%	19%	17×
Kano	83%	17%	13×
Gombe	88%	12%	10×
Kaduna	84%	16%	9×

The pattern is clear but imperfect — Niger’s 85% incremental coverage is similar to Guinea’s 82%, yet Niger scores 7x higher because its mortality rate is higher (1.4% vs 1.1%), VAS effect is double (11.1% vs 5.5%), and cost per child is 70% lower. The counterfactual sets the scale; the other parameters determine the multiplier within it.

Helen Keller’s leverage/funging adjustments need careful reading. In my first pass, the funging adjustment for Burkina Faso extracted as 531.99 instead of -0.431. These values come from separate rows in the spreadsheet that are easy to confuse — a “percentage change” row vs. an “adjusted value” row.

Verification

Parameters are extracted from GiveWell’s November 2025 CEA spreadsheets (AMF, MC, HKI, NI). I verified all 46 charity/country final cost-effectiveness multiples against the spreadsheets:

Charity	Countries	Max difference
Against Malaria Foundation	8	<0.001%
Malaria Consortium	8	<0.001%
Helen Keller International	8	<0.001%
New Incentives	9	0.000% (exact)
Deworm the World	13	0.000% (exact)

GiveDirectly uses older parameter extractions. 298 automated tests verify the calculations. The remaining <0.001% differences for AMF/MC/HKI are floating-point precision, not model discrepancies.

Interactive features

Calculation breakdown: Click any country to see the step-by-step calculation with every intermediate value. Click any highlighted number to edit it.

Moral weights: GiveWell’s default weights peak at ages 5-9 (134) and weight under-5 at 116. You can adjust these with a single multiplier or set each age bracket independently. Charities with different age profiles (AMF and MC focus on under-5; NI and HKI have broader age effects) shift rankings when you change these.

Sensitivity analysis: Sweep any moral weight parameter across its range and see how all six charities’ cost-effectiveness changes. This makes crossover points visible — for instance, the under-5 weight where NI overtakes MC, or the discount rate at which deworming drops below cash transfers.

How assumptions affect rankings

A few examples of what you see when you adjust parameters:

Default rankings (best country per charity, GiveWell Nov 2025 defaults):

Rank	Charity	Best country	x benchmark
1	Helen Keller	Niger	79×
2	New Incentives	Sokoto	39×
3	AMF	Guinea	23×
4	Malaria Consortium	Chad	15×
5	GiveDirectly	Mozambique	4×

Double the under-5 moral weight. All four mortality-focused charities see exactly +100% gains — their value comes entirely from deaths averted, so doubling the weight doubles the result. GiveDirectly gains only +3.4% because its value comes from consumption benefits, not mortality. Rankings don’t change.

Equal moral weights across ages (all set to 100). This reflects a view that a life saved is equally valuable at any age. It penalizes child-focused charities by 14-16% (since the default under-5 weight of ~116 drops to 100). Helen Keller drops from 79× to 67×. But rankings remain the same.

Double AMF’s cost per child in DRC. The relationship is perfectly linear: doubling cost halves the x benchmark from 14.6× to 7.3×. This is a more powerful lever for changing relative rankings within mortality-focused charities than moral weight changes, which scale all of them equally.

The main takeaway: rankings are remarkably stable to moral weight changes because the top charities all work through similar mortality-reduction mechanisms. Operational cost differences between countries create much more variation than ethical assumptions.

Programmatic access

The models are pure TypeScript functions — no UI dependency. Clone the repo and run analyses directly:

git clone https://github.com/MaxGhenis/givewell-cea && cd givewell-cea
bun install

Sweep a parameter:

// save as sweep.ts, run with: bunx tsx sweep.ts
import { calculateHelenKeller } from "./src/lib/models/helen-keller";
import { HK_COUNTRY_PARAMS } from "./src/lib/models/countries";

const niger = HK_COUNTRY_PARAMS.niger;
for (let effect = 0.05; effect <= 0.2; effect += 0.05) {
  const r = calculateHelenKeller({
    grantSize: 1_000_000,
    ...niger,
    vasEffect: effect,
  });
  console.log(`VAS effect ${(effect * 100).toFixed(0)}% → ${r.finalXBenchmark.toFixed(1)}×`);
}
// VAS effect 5%  → 35.6×
// VAS effect 10% → 71.3×
// VAS effect 15% → 106.9×
// VAS effect 20% → 142.5×

Rank all charity/country combinations:

// save as rank.ts, run with: bunx tsx rank.ts
import { calculateHelenKeller } from "./src/lib/models/helen-keller";
import { calculateAMF } from "./src/lib/models/amf";
import { calculateNewIncentives } from "./src/lib/models/new-incentives";
import {
  HK_COUNTRY_PARAMS, HK_COUNTRY_NAMES,
  AMF_COUNTRY_PARAMS, AMF_COUNTRY_NAMES,
  NI_COUNTRY_PARAMS, NI_COUNTRY_NAMES,
} from "./src/lib/models/countries";

const G = 1_000_000;
const all = [
  ...Object.entries(AMF_COUNTRY_PARAMS).map(([k, v]) => ({
    charity: "AMF", country: AMF_COUNTRY_NAMES[k],
    xb: calculateAMF({ grantSize: G, ...v }).finalXBenchmark,
  })),
  ...Object.entries(HK_COUNTRY_PARAMS).map(([k, v]) => ({
    charity: "HKI", country: HK_COUNTRY_NAMES[k],
    xb: calculateHelenKeller({ grantSize: G, ...v }).finalXBenchmark,
  })),
  ...Object.entries(NI_COUNTRY_PARAMS).map(([k, v]) => ({
    charity: "NI", country: NI_COUNTRY_NAMES[k],
    xb: calculateNewIncentives({ grantSize: G, ...v }).finalXBenchmark,
  })),
];
all.sort((a, b) => b.xb - a.xb);
for (const r of all.slice(0, 10)) {
  console.log(`${r.charity.padEnd(4)} ${r.country.padEnd(16)} ${r.xb.toFixed(1)}×`);
}
// HKI  Niger            79.1×
// NI   Sokoto           38.6×
// NI   Zamfara          31.3×
// HKI  DRC              29.9×
// NI   Kebbi            29.0×
// AMF  Guinea           22.8×
// ...

Each model function (calculateAMF, calculateHelenKeller, calculateNewIncentives, etc.) takes a flat parameter object and returns all intermediate values, so you can inspect any step of the pipeline.

Limitations

This replicates the structure of GiveWell’s models but not their full analytical process. Specifically:

• I implement the calculation pipeline but not the reasoning behind parameter choices. GiveWell’s adjustments (charity quality, external validity, leverage, funging) encode substantial judgment that this tool takes as given.
• No uncertainty analysis. The tool shows point estimates only. Several excellent posts have explored what happens when you put distributions around these estimates.
• The tool currently covers GiveWell’s top 6 charities but not newer additions.
• GiveDirectly and deworming use older parameter extractions.

For donation decisions, use GiveWell’s published estimates.

Source code: github.com/MaxGhenis/givewell-cea