Econml Causal Guide

What it does

When your agent uses it

• Tasks that involve Econometrics and empirical research
• Tasks that involve Machine learning

Example prompts

• “/econml-causal-guide”

Requirements

• Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Always validate assumptions: DML requires conditional ignorability (selection on observables). Document your identification strategy…
2. 2Cross-fitting is essential: Never skip the cross-fitting step, as it prevents overfitting bias in the nuisance estimates.
3. 3Report multiple estimators: Present results from DML, DR Learner, and Causal Forest side by side to assess robustness.
4. 4Check overlap: Verify sufficient overlap in covariate distributions between treated and control groups before estimation.
5. 5Use honest estimation: EconML Causal Forests use sample splitting for honesty by default, ensuring valid inference.

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • pip

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • github.com
  • econml.azurewebsites.net

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Econml Causal Guide loads about 1.8k tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 570 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~24

When it runs · the whole SKILL.md, loaded when a task matches

~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 570 words, ~1,789 tokens.

name

econml-causal-guide

description

Apply EconML for causal inference combining machine learning and econometrics

pip install econml

pip install econml[all]

import econml
print(econml.__version__)

from econml.dml import LinearDML
from econml.orf import DMLOrthoForest
print("EconML loaded successfully")

from econml.dml import LinearDML, CausalForestDML
from sklearn.ensemble import GradientBoostingRegressor

# Linear DML for parametric treatment effect estimation
est = LinearDML(
    model_y=GradientBoostingRegressor(),
    model_t=GradientBoostingRegressor(),
    cv=5,
    random_state=42
)
est.fit(Y, T, X=X, W=W)

# Get treatment effect estimates with confidence intervals
effect = est.effect(X_test)
ci = est.effect_interval(X_test, alpha=0.05)
print(f"ATE: {est.ate():.4f}")
print(f"ATE 95% CI: {est.ate_interval(alpha=0.05)}")

from econml.dml import CausalForestDML

cf_est = CausalForestDML(
    model_y=GradientBoostingRegressor(),
    model_t=GradientBoostingRegressor(),
    n_estimators=200,
    min_samples_leaf=10,
    cv=5,
    random_state=42
)
cf_est.fit(Y, T, X=X, W=W)

# Heterogeneous treatment effects
hte = cf_est.effect(X_test)
# Feature importance for treatment effect heterogeneity
importances = cf_est.feature_importances_

from econml.dr import DRLearner
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor

dr_est = DRLearner(
    model_propensity=RandomForestClassifier(),
    model_regression=RandomForestRegressor(),
    model_final=RandomForestRegressor(),
    cv=5
)
dr_est.fit(Y, T, X=X, W=W)

from econml.iv.dml import DMLIV

iv_est = DMLIV(
    model_y_xw=GradientBoostingRegressor(),
    model_t_xw=GradientBoostingRegressor(),
    model_t_xwz=GradientBoostingRegressor(),
    cv=5
)
iv_est.fit(Y, T, Z=Z, X=X, W=W)

# Identify subgroups with largest treatment effects
import pandas as pd

effects_df = pd.DataFrame({
    "effect": cf_est.effect(X_test).flatten(),
    "ci_lower": cf_est.effect_interval(X_test, alpha=0.05)[0].flatten(),
    "ci_upper": cf_est.effect_interval(X_test, alpha=0.05)[1].flatten()
}, index=X_test.index)

# Top beneficiaries
top_group = effects_df.nlargest(100, "effect")

© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/analysis/econometrics/econml-causal-guide of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

• brycewang-stanford/Auto-Empirical-Research-Skillsskills/43-wentorai-research-plugins/skills/analysis/econometrics/econml-causal-guide