Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add CUHK-AIM-Group/NeuroClaw --skill causal-treatment-modelsgit clone --depth 1 https://github.com/CUHK-AIM-Group/NeuroClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/causal-treatment-models)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/causal-treatment-models"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/causal-treatment-models/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/causal-treatment-models"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/causal-treatment-models.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00116 | $0.01092 |
| Opus 5 | $0.00058 | $0.00546 |
| Sonnet 5 | $0.00023 | $0.00218 |
| Haiku 4.5 | $0.00012 | $0.00109 |
Grade A, and why
causal-treatment-models scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
Installation
pip install numpy pandas scipy scikit-learn joblib torch
For Causal Forest:
pip install econml
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
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 154 lines · 116 tokens per session scan A b2292cd81f3a
causal-treatment-models is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (84 stars, last pushed 5d ago), licensed MIT. It adds 116 tokens to every session and 1,092 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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