Causal Treatment Models Skill

Overview

causal-treatment-models estimates average or conditional treatment effects from observational subject-level features. It is for the contrast Y(1) - Y(0), not for predicting the observed outcome alone.

Supported estimators

Model	Output
ipw	propensity-weighted ATE as constant CATE
s_learner	single outcome model treatment contrast
t_learner	separate treated/control outcome models
x_learner	imputed effects blended by propensity
doubly_robust	doubly robust pseudo-outcome CATE
policy_learner	interpretable treatment assignment policy
causal_forest	econml CausalForestDML
tarnet	shared representation with two outcome heads
dragonnet	TARNet plus propensity head

The CLI uses cross-fitted held-out predictions. Causal interpretation still requires consistency, positivity, no unmeasured confounding, and a defensible temporal ordering.

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Installation

pip install numpy pandas scipy scikit-learn joblib torch

For Causal Forest:

pip install econml

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Workflows

1. Prepare treatment data

subject_id,treatment,response,age,sex,baseline_score,roi_001
sub-001,1,4.2,64,0,18.0,0.12
sub-002,0,1.7,59,1,17.5,0.08

Treatment must be binary for the current CLI. Include only pretreatment covariates in the feature matrix.

2. Doubly robust CATE

python skills/causal-treatment-models/scripts/train_reference.py \
  --features treatment.csv \
  --treatment-col treatment \
  --outcome-col response \
  --subject-col subject_id \
  --model doubly_robust \
  --folds 5 \
  --output-dir run_models_output/treatment_dr

3. Neural treatment-effect model

python skills/causal-treatment-models/scripts/train_reference.py \
  --features treatment.csv \
  --treatment-col treatment \
  --outcome-col response \
  --model dragonnet \
  --epochs 200 \
  --device cuda \
  --output-dir run_models_output/dragonnet

4. Causal Forest or policy learning

Use --model causal_forest for nonlinear heterogeneous effects and --model policy_learner for a compact treatment rule. Report overlap, propensity distributions, standardized mean differences, ATE/CATE uncertainty, and policy value under an explicit evaluation design.

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Input / Output Summary

Item	Format
Input	CSV with subject, treatment, outcome, pretreatment covariates
Treatment	binary 0/1
Predictions	predictions.csv with held-out CATE and policy
Fold membership	fold_assignments.csv
Metrics	metrics.json with effect/policy summaries
Checkpoint	checkpoint.joblib
Provenance	config.json, run_manifest.json

Do not describe a high predictive score as causal evidence. Negative controls, sensitivity analyses, and randomized validation remain separate requirements.

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Testing

pytest models/tests/test_extended_models.py -q
python skills/causal-treatment-models/scripts/train_reference.py --help

causal_forest tests require the optional econml installation.

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Directory Reference

models/causal_treatment/
├── estimators.py       IPW, meta-learners, DR, policy, forest, neural models
└── train.py            cross-fitted CLI

skills/causal-treatment-models/
├── SKILL.md
└── scripts/train_reference.py

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Reference

• Shalit et al. representation learning for individual treatment effects (2017).
• Shi et al. DragonNet for targeted regularization (2019).
• econml provides the optional CausalForestDML backend.

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Created At: 2026-07-26 HKT Last Updated At: 2026-07-29 HKT Author: chengwang96