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Method library WORKFLOW / 014
Research workflow/5 steps

Qini curves: automatic cost-benefit analysis

@grf ✓ P D · Jun 29, 2026 · 788 views
Causal ForestHeterogeneous Effects
Source grf — Athey, Tibshirani & Wager
In the margins · Peer notesA question, a caveat, a different perspective. Start a conversation here. 0 discussion threads
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01
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Method mapInput → 5 steps → output
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Step 1 of 5
01
Estimation

[GRF] Causal forest

Step notesField note & discussion1

The core estimate — where the causal quantity itself is computed.

What happens here

Fit the forest and produce per-unit CATEs and AIPW scores.

Formula
\tau(x)=\mathbb{E}\!\left[\,Y(1)-Y(0)\mid X=x\,\right]
The estimator

Causal forest — Honest random forest for heterogeneous treatment effects — CATE for a binary treatment via GRF moment conditions.

CATE Code @ v2.6.1 ↗ Paper ↗
Reads fromNot specified Feeds into#2#3
Key code 
cf <- causal_forest(X, Y, W)          # Y.hat, W.hat cross-fit
tau.hat <- predict(cf)$predictions    # OOB CATEs
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StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore
  1. Which assumption matters most here, and what evidence could challenge it?
  2. What would you check before passing this output to the next step?

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02
Data contract

Inputs & data

CATEs for one or more treatment arms, plus per-unit-per-arm costs.

Show data format & exampleHide example

Format — one row per unit. Covariates X, a treatment factor W with K arms, and outcome Y; optionally a per-arm cost.

  X1    X2   W       Y
 0.4  -1.1  armA   3.1
-0.1   0.6  ctrl   1.8
 1.2   0.3  armB   4.0
03
Read the evidence

Results & interpretation

5 figures Workflow discussion 0
A Qini curve vs random targeting — value gained at each level of spend. View figure
01FigureA Qini curve vs random targeting — value gained at each level of spend.
TOC vs Qini: two views of the same prioritisation, one per-unit, one cumulative. View figure
02FigureTOC vs Qini: two views of the same prioritisation, one per-unit, one cumulative.
A multi-armed Qini — allocating a fixed budget across several competing treatments. View figure
03FigureA multi-armed Qini — allocating a fixed budget across several competing treatments.
Single- vs multi-armed policy value at matched spend. View figure
04FigureSingle- vs multi-armed policy value at matched spend.
The area between two Qini curves quantifies which policy is better, and by how much. View figure
05FigureThe area between two Qini curves quantifies which policy is better, and by how much.

Figures reproduced from grf — Athey, Tibshirani & Wager — unofficial community showcase; all credit to the original authors.

Context & interpretation

The GRF 'Qini curves' tutorial. Targeting in the real world has a budget and per-unit costs; the maq sister package turns CATEs into a Pareto frontier of expected-gain vs spend so you can pick the operating point honestly. Unofficial summary.

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IN THE MARGINS

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