Python Causality Guide

What it does

When your agent uses it

• Tasks that involve Econometrics and empirical research

Example prompts

• “/python-causality-guide”

Requirements

• Python 3

Workflow steps

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

1. 1Always graph your data first: Visual inspection reveals patterns that inform modeling choices and expose violations of identifying…
2. 2Understand your identification strategy: Before running any estimator, articulate clearly what variation identifies the causal effect and…
3. 3Cluster standard errors appropriately: When treatment is assigned at group level, cluster standard errors at that level to avoid…
4. 4Run robustness checks: Vary specifications, bandwidths, control variables, and functional forms to assess sensitivity of conclusions.
5. 5Report effect sizes alongside p-values: Statistical significance without practical significance is not informative for policy or…

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
  • git
  • python
  • jupyter

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

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • github.com

  Also links to:

  • matheusfacure.github.io

  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

Python Causality Guide loads about 1.7k tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 615 words of instructions outside code blocks.

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

~23

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

~1.7k

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). 615 words, ~1,739 tokens.

name

python-causality-guide

description

Learn causal inference with Python using the Brave and True handbook

git clone https://github.com/matheusfacure/python-causality-handbook.git
cd python-causality-handbook

# Create a virtual environment
python -m venv causal-env
source causal-env/bin/activate

# Install dependencies
pip install numpy pandas matplotlib seaborn scikit-learn statsmodels
pip install linearmodels causalinference
pip install jupyter

jupyter notebook

import pandas as pd
import numpy as np
from scipy.stats import ttest_ind

# Estimate ATE from randomized experiment
treated = data[data["treatment"] == 1]["outcome"]
control = data[data["treatment"] == 0]["outcome"]
ate = treated.mean() - control.mean()
t_stat, p_value = ttest_ind(treated, control)
print(f"ATE: {ate:.3f}, p-value: {p_value:.4f}")

import statsmodels.formula.api as smf

# OLS with controls
model = smf.ols("outcome ~ treatment + age + income + education", data=data)
results = model.fit(cov_type="HC1")
print(results.summary().tables[1])

from linearmodels.iv import IV2SLS

# Two-stage least squares
iv_formula = "outcome ~ 1 + [treatment ~ instrument]"
iv_model = IV2SLS.from_formula(iv_formula, data=data)
iv_results = iv_model.fit(cov_type="robust")
print(iv_results.summary)

# Difference-in-Differences with two-way fixed effects
did_model = smf.ols(
    "outcome ~ treated_post + C(unit_id) + C(time_period)",
    data=panel_data
)
did_results = did_model.fit(cov_type="cluster", cov_kwds={"groups": panel_data["unit_id"]})

© 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/python-causality-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/python-causality-guide