Method library WORKFLOW / 005

Research workflow/8 steps

Heterogeneous treatment effects with a causal forest (GRF recipe)

@grf ✓ P D · Jun 29, 2026 · 1.3k views

@misc{grf,
  title        = {grf},
  author       = {Athey and Tibshirani and Wager},
  howpublished = {\url{https://grf-labs.github.io/grf/}},
  note         = {Software / documentation}
}

In the margins · Peer notes@aipw_amir · The fact that one causal_forest object feeds all four downstream branches is the whole pitch for GRF. Compose, don… 3

The method workspace

Follow the analysis.

Select a node to explore the method

Method mapInput → 8 steps → output

START WITHInput data

ARRIVE ATResults

Read step 01

Inside the methodReasoning, equations & implementation

Data prep

[GRF] Regression forest

Step notesField note & discussion1

Data preparation — shapes the raw inputs into what the estimator expects.

What happens here

Cross-fit Y.hat = E[Y|X] and W.hat = E[W|X] so the causal forest splits on orthogonalized residuals.

Formula

\hat\mu(x)=\mathbb{E}\!\left[\,Y\mid X=x\,\right]

The estimator

Regression forest — Honest non-parametric regression for E[Y|X], with out-of-bag predictions and pointwise CIs.

OTHER Code @ v2.6.1 ↗ Paper ↗

Reads fromNot specified Feeds into#2

Key code

rf <- regression_forest(X, Y)
Y.hat <- predict(rf)$predictions

Discussion on this step (0)

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

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

Peer discussion

Bring your experience, a question, or a different perspective.

Estimation

[GRF] Causal forest

Step notesField note & discussion1

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

What happens here

Grow the causal forest on the residualized moments; get OOB CATEs.

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 from#1 Feeds into#3#4#5#6

Key code

cf <- causal_forest(X, Y, W)          # Y.hat, W.hat cross-fit
tau.hat <- predict(cf)$predictions    # OOB CATEs

Discussion on this step (0)

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

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

Peer discussion

Bring your experience, a question, or a different perspective.

Diagnostic / pre-tests

test_calibration()

Step notesField note & discussion1

A pre-flight check — run this before trusting any estimate downstream.

What happens here

Regress AIPW scores on mean-forest and differential-forest predictions; the two coefficients should be ≈ 1.

Formula

\hat\tau(X)=\alpha\,\bar\tau+\beta\,(\hat\tau(X)-\bar\tau)+\varepsilon\quad(\alpha,\beta\approx1\ \text{if calibrated})

Reads from#2 Feeds into#8

Key code

test_calibration(cf)   # mean & differential forest coefficients ≈ 1?

Reference / docs ↗

Discussion on this step (0)

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

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

Peer discussion

Bring your experience, a question, or a different perspective.

Inference

[GRF] AIPW average treatment effect

Step notesField note & discussion1

Uncertainty quantification — standard errors, intervals, and aggregation.

What happens here

Doubly-robust ATE (and ATT) as the headline number.

Formula

\hat\tau=\tfrac1n\textstyle\sum_i\hat\Gamma_i,\ \ \hat\Gamma_i=\hat\mu_1(X_i)-\hat\mu_0(X_i)+\tfrac{W_i(Y_i-\hat\mu_1)}{\hat e(X_i)}-\tfrac{(1-W_i)(Y_i-\hat\mu_0)}{1-\hat e(X_i)}

The estimator

AIPW average treatment effect — Doubly-robust ATE / ATT / ATC / overlap-weighted effect from a trained causal forest, via augmented IPW.

ATE Code @ v2.6.1 ↗ Paper ↗

Reads from#2 Feeds into#8

Key code

average_treatment_effect(cf, target.sample = "all")      # ATE
average_treatment_effect(cf, target.sample = "treated")  # ATT

Discussion on this step (0)

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

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

Peer discussion

Bring your experience, a question, or a different perspective.

Heterogeneity

best_linear_projection()

Step notesField note & discussion1

Heterogeneity — who is affected, and by how much, not just on average.

What happens here

Project the CATE onto a few interpretable covariates to describe who benefits.

Formula

\mathbb{E}\!\left[\,\tau(X)\mid A\,\right]=A^\top\beta

Reads from#2 Feeds into#8

Key code

best_linear_projection(cf, X[, c("age", "prior")])

Reference / docs ↗

Discussion on this step (0)

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

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

Peer discussion

Bring your experience, a question, or a different perspective.

Heterogeneity

[GRF] Rank-weighted ATE — RATE / AUTOC / Qini

Step notesField note & discussion1

Heterogeneity — who is affected, and by how much, not just on average.

What happens here

AUTOC on a held-out evaluation forest: is the heterogeneity real and useful for targeting?

Formula

\mathrm{AUTOC}=\int_0^1\!\mathrm{TOC}(q)\,dq,\quad \mathrm{TOC}(q)=\mathbb{E}\!\left[\tau(X)\mid \hat S(X)\ge \hat F^{-1}(1-q)\right]-\mathbb{E}[\tau(X)]

The estimator

Rank-weighted ATE — RATE / AUTOC / Qini — Evaluate how well a CATE estimate prioritizes treatment: TOC curve, AUTOC and Qini with confidence intervals.

OTHER Code @ v2.6.1 ↗ Paper ↗

Reads from#2 Feeds into#7

Key code

rate <- rank_average_treatment_effect(eval.forest, priorities = tau.hat)
plot(rate)                          # TOC curve
rate$estimate / rate$std.err        # AUTOC z-stat

Discussion on this step (0)

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

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

Peer discussion

Bring your experience, a question, or a different perspective.

Robustness check

Policy learning (policytree)

Step notesField note & discussion1

A robustness check — does the headline result survive a different lens?

What happens here

Learn a depth-2 optimal assignment tree from the doubly-robust scores.

Formula

\hat\pi=\arg\max_{\pi\in\Pi}\ \tfrac1n\textstyle\sum_i\Gamma_i\big(\pi(X_i)\big)

Reads from#6 Feeds into#8

Key code

library(policytree)
tree <- policy_tree(X, dr.scores, depth = 2)
plot(tree)

Reference / docs ↗

Discussion on this step (0)

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

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

Peer discussion

Bring your experience, a question, or a different perspective.

Reporting

CATE histogram + targeting report

Step notesField note & discussion1

Reporting — turn the numbers into a figure or table a reader can act on.

What happens here

Distribution of τ̂(x), BLP table, TOC curve and the learned policy, side by side.

Formula

\hat\tau_{\text{target}}=\textstyle\sum_i w_i\hat\Gamma_i,\quad w_i\propto \tfrac{p_{\text{target}}(X_i)}{p_{\text{source}}(X_i)}

Reads from#3#4#5#7 Feeds intoNot specified

Key code

average_treatment_effect(cf, target.weights = w_target)

Discussion on this step (0)

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

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

Peer discussion

Bring your experience, a question, or a different perspective.

Data contract

Inputs & data

A randomized/observational study: covariate matrix X, binary treatment W, outcome Y.

Show data format & exampleHide example

Format — one row per unit. A covariate matrix X (numeric), a binary treatment W ∈ {0,1}, and an outcome Y.

  X1     X2    X3    W    Y
 0.42  -1.1   0     1   3.10
-0.07   0.6   1     0   1.85
 1.20   0.3   0     1   4.02

Read the evidence

Results & interpretation

4 figures Workflow discussion 3

01FigureOut-of-bag CATE estimates τ̂(x) across the sample — the spread is the heterogeneity you're after.

02FigureHow a causal forest splits: it carves out neighbourhoods that maximise the contrast in treatment effect.

03FigureTargeting operator characteristic (TOC) when units are ranked by the financial-autonomy effect.

04FigureTOC for the cognitive-impairment outcome — benefit concentrates in the top-ranked units.

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

Context & interpretation

What the GRF docs recommend end-to-end for credible heterogeneous-effect analysis. Nuisances are cross-fit first; the causal forest is then validated (calibration), summarized (AIPW ATE, best linear projection), stress-tested for useful heterogeneity (RATE/AUTOC), and finally turned into a targeting policy. Unofficial summary of the public GRF tutorials.

Peer notes

Discuss the workflow (3)

6

@calibrator_cleo · Jun 29, 2026

This is the canonical order and I wish more papers followed it. Calibration BEFORE you start interpreting subgroups, every time.

2

@hte_hannah · Jun 29, 2026

And RATE before policy learning — no point optimizing a rule on heterogeneity that isn't there.
3

@targeting_tara · Jun 29, 2026

Bookmarking this as the onboarding doc for new analysts. The fan-out from the forest into ATE / BLP / RATE / policy is exactly how I teach it.
6

@aipw_amir · Jun 29, 2026

The fact that one causal_forest object feeds all four downstream branches is the whole pitch for GRF. Compose, don't re-fit.