Senior Data Scientist
borghei/Claude-Skills
A skill your agent uses when the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model…
Econometrics skill for machine learning methods in causal inference.
Install ML Causal
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ml-causal -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills ml-causal --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/67-econfin-workflow-toolkit/ml-causal .claude/skills/ml-causal && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ml-causal" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/ml-causal into .claude/skills/ml-causal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-causal", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/ml-causalType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ml-causal -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills ml-causal --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/67-econfin-workflow-toolkit/ml-causal .agents/skills/ml-causal && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ml-causal" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/ml-causal into .agents/skills/ml-causal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-causal", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ml-causal -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills ml-causal --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/67-econfin-workflow-toolkit/ml-causal .cursor/skills/ml-causal && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ml-causal" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/ml-causal into .cursor/skills/ml-causal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-causal", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git --path skills/67-econfin-workflow-toolkit/ml-causal--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ml-causal -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills ml-causal --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/67-econfin-workflow-toolkit/ml-causal .gemini/skills/ml-causal && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ml-causal" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/ml-causal into .gemini/skills/ml-causal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-causal", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills ml-causalInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ml-causal -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/67-econfin-workflow-toolkit/ml-causal .github/skills/ml-causal && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ml-causal" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/ml-causal into .github/skills/ml-causal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-causal", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ml-causal -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills ml-causal --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/67-econfin-workflow-toolkit/ml-causal .opencode/skills/ml-causal && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ml-causal" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/ml-causal into .opencode/skills/ml-causal/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-causal", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
ml-causal- GitHub stars
- 4.5k
- Token cost
- ~4k tokens
- SKILL.md length
- 706 words
- Files
- 2 (incl. references)
- Skills in repo
- 383
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Econometrics skill for machine learning methods in causal inference.
- Works in 4 steps: Cross-fitting: Split sample into K folds… → Nuisance estimation: On each fold k, use… → Residualize: Compute Ỹ = Y − ĝ(X) and D̃… → …
- Asks about: causal forest
- SKILL.md covers When to Use ML Causal Methods, Double/Debiased Machine…, Causal Forest (Generalized… and BLP Analysis (Best Linear…, plus 8 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
ML Causal is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. Econometrics skill for machine learning methods in causal inference. Activates when the user asks about: "causal forest", "generalized random forest", "GRF", "double machine learning", "DML", "debiased machine learning", "LASSO for variable selection", "post-LASSO", "heterogeneous treatment effects", "CATE", "conditional average treatment effect", "BLP analysis", "CLAN analysis", "causal tree", "honest estimation", "因果森林", "双重机器学习", "异质性处理效应", "条件平均处理效应", "LASSO变量选择", "机器学习因果推断", "去偏机器学习"
Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/ml-causal-reference.md`).
It sits in Data & Analytics, covering Machine learning and Econometrics and empirical research. The repository describes itself as: 🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库，覆盖8大社会科学学科的实证研究。CoPaper.AI…
When your agent uses it
- Asks about: causal forest
- Generalized random forest
- Double machine learning
- Debiased machine learning
Example prompts
- “causal forest”
- “generalized random forest”
- “double machine learning”
- “/ml-causal”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Cross-fitting: Split sample into K folds (typically K=5)
- Nuisance estimation: On each fold k, use remaining folds to estimate ĝ(X) and m̂(X) using ML
- Residualize: Compute Ỹ = Y − ĝ(X) and D̃ = D − m̂(X)
- Final estimation: Regress Ỹ on D̃ to obtain θ̂
What it can do on your machine
Read from SKILL.md and the folder at commit 9fa87d8. 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
No scripts in the folder and no shell commands in SKILL.md (its code samples are r, python and stata).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
ML Causal loads about 4k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 126 tokens; SKILL.md has 706 words of instructions outside code blocks.
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
Auto-check passed
The automated check found no risky patterns in SKILL.md.
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
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 706 words (~3,991 tokens).
“This skill covers modern ML-based causal inference methods: Causal Forests (GRF) for heterogeneous treatment effects, Double/Debiased Machine Learning (DML) for partially linear models, and LASSO-based variable selection. These methods combine the flexibility of ML with the rigor of econometric identification.”
- name
- ml-causal
Files
SKILL.md and 1 other file (references) in skills/67-econfin-workflow-toolkit/ml-causal of brycewang-stanford/Auto-Empirical-Research-Skills.
- SKILL.md
- references/ml-causal-reference.md
Open the folder on GitHubat commit 9fa87d8
Compare with similar skills
ML Causal next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| ML Causal this skillbrycewang-stanford/Auto-Empirical-Research-Skills | 4.5k | — | ~4k | Automated safety check: Pass | Custom licence | |
| Senior Data Scientistborghei/Claude-Skills | 886 | — | ~1.7k | Automated safety check: Pass | MIT | |
| Senior Data Scientistalirezarezvani/claude-skills | 28k | 1 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Empirical Analysis Skill PythonDrchronx/ai-agent-research-starter-kit | 137 | — | ~3k | Automated safety check: Pass | Custom licence | |
| Econml Causal Guidewentorai/research-plugins | 298 | 1 repos | ~1.8k | Automated safety check: Pass | MIT | |
| Modeling Strategy Guidewentorai/research-plugins | 298 | 1 repos | ~2.2k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about ML Causal
What does ML Causal do?
Econometrics skill for machine learning methods in causal inference. ML Causal is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. Econometrics skill for machine learning methods in causal inference.
When should I use ML Causal?
ML Causal fits situations like: asks about: causal forest; generalized random forest; double machine learning; debiased machine learning.
How do I install ML Causal in Claude Code?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ml-causal -a claude-code`. Or copy the skill folder (skills/67-econfin-workflow-toolkit/ml-causal in brycewang-stanford/Auto-Empirical-Research-Skills) into .claude/skills/ml-causal in your project. Claude Code loads it when a task matches its description.
How do I install ML Causal in Codex?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ml-causal -a codex`. Or copy the skill folder (skills/67-econfin-workflow-toolkit/ml-causal in brycewang-stanford/Auto-Empirical-Research-Skills) into .agents/skills/ml-causal in your project. Codex loads it when a task matches its description.
Can I use ML Causal in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill ml-causal -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ml-causal, .gemini/skills/ml-causal, .github/skills/ml-causal and .opencode/skills/ml-causal in your project.
What does ML Causal need to run?
SKILL.md names no scripts, command-line tools or credentials: ML Causal is instructions for the agent only. Our summary lists: Python 3.
Does ML Causal access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is ML Causal safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does ML Causal use?
ML Causal has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
How many tokens does ML Causal use?
About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.3k tokens, read only when the agent opens those files.
What are the alternatives to ML Causal?
Skills that share tags, products or a category with ML Causal: Senior Data Scientist (borghei/Claude-Skills, 886 stars), Senior Data Scientist (alirezarezvani/claude-skills, 28k stars), Empirical Analysis Skill Python (Drchronx/ai-agent-research-starter-kit, 137 stars) and Econml Causal Guide (wentorai/research-plugins, 298 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains ML Causal?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Auto-Empirical-Research-Skills, which has 4,542 GitHub stars. The repository holds 383 skills in this directory. The repository was last updated on October 5, 2026.
Source: brycewang-stanford/Auto-Empirical-Research-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.