@misc{grf,
title = {grf},
author = {Athey and Tibshirani and Wager},
howpublished = {\url{https://grf-labs.github.io/grf/}},
note = {Software / documentation}
}Follow the analysis.
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[GRF] Causal forest
The core estimate — where the causal quantity itself is computed.
Fit with OOB predictions and variance.
Causal forest — Honest random forest for heterogeneous treatment effects — CATE for a binary treatment via GRF moment conditions.
cf <- causal_forest(X, Y, W) # Y.hat, W.hat cross-fit
tau.hat <- predict(cf)$predictions # OOB CATEs
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?
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Peer discussion
- Bring your experience, a question, or a different perspective.
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test_calibration()
A pre-flight check — run this before trusting any estimate downstream.
Mean-forest coefficient ≈ 1 says the average ATE is right; differential-forest coefficient ≈ 1 says heterogeneity is real.
test_calibration(cf) # mean & differential forest coefficients ≈ 1?
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?
Choose a question to start your comment.
Peer discussion
- Bring your experience, a question, or a different perspective.
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variable_importance()
A pre-flight check — run this before trusting any estimate downstream.
Which covariates the forest split on most often — sanity-check against domain knowledge.
variable_importance(cf)
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?
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Peer discussion
- Bring your experience, a question, or a different perspective.
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best_linear_projection()
Heterogeneity — who is affected, and by how much, not just on average.
Linear projection of τ̂(x) on a hand-picked subset for human-readable heterogeneity.
best_linear_projection(cf, X[, c("age", "prior")])
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?
Choose a question to start your comment.
Peer discussion
- Bring your experience, a question, or a different perspective.
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OOB residual checks
A pre-flight check — run this before trusting any estimate downstream.
Plot OOB CATEs against propensity & key covariates; large structure left here = under-fitting.
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?
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Peer discussion
- Bring your experience, a question, or a different perspective.
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Fit-evaluation report
Reporting — turn the numbers into a figure or table a reader can act on.
Calibration table + importance bar chart + BLP table + residual scan, in one place.
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?
Choose a question to start your comment.
Peer discussion
- Bring your experience, a question, or a different perspective.
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Inputs & data
A fitted causal forest (n=2000, p=10) you want to validate before trusting.
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
Figures reproduced from grf — Athey, Tibshirani & Wager — unofficial community showcase; all credit to the original authors.
The GRF 'Evaluating a causal forest fit' tutorial. Before reading anything off a fitted forest, check that it's well-calibrated and that the heterogeneity isn't an artifact of noise. Then look at what's driving the splits and how the CATE relates to interpretable covariates. Unofficial summary.
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