Senior Data Scientist
alirezarezvani/claude-skills
World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics.
This skill covers causal machine learning methods in applied economics and quantitative social science.
Install Causal ML
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-ml -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills causal-ml --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/11-James-Traina-compound-science/skills/causal-ml .claude/skills/causal-ml && 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 "causal-ml" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/causal-ml into .claude/skills/causal-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "causal-ml", 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/11-James-Traina-compound-science/skills/causal-mlType 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 causal-ml -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills causal-ml --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/11-James-Traina-compound-science/skills/causal-ml .agents/skills/causal-ml && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "causal-ml" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/causal-ml into .agents/skills/causal-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "causal-ml", 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 causal-ml -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills causal-ml --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/11-James-Traina-compound-science/skills/causal-ml .cursor/skills/causal-ml && 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 "causal-ml" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/causal-ml into .cursor/skills/causal-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "causal-ml", 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/11-James-Traina-compound-science/skills/causal-ml--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 causal-ml -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills causal-ml --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/11-James-Traina-compound-science/skills/causal-ml .gemini/skills/causal-ml && 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 "causal-ml" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/causal-ml into .gemini/skills/causal-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "causal-ml", 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 causal-mlInstalls 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 causal-ml -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/11-James-Traina-compound-science/skills/causal-ml .github/skills/causal-ml && 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 "causal-ml" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/causal-ml into .github/skills/causal-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "causal-ml", 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 causal-ml -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 causal-ml --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/11-James-Traina-compound-science/skills/causal-ml .opencode/skills/causal-ml && 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 "causal-ml" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/11-James-Traina-compound-science/skills/causal-ml into .opencode/skills/causal-ml/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "causal-ml", 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
causal-ml- GitHub stars
- 4.5k
- Token cost
- ~2.2k tokens
- SKILL.md length
- 788 words
- Files
- 6 (incl. references)
- Skills in repo
- 383
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
This skill covers causal machine learning methods in applied economics and quantitative social science.
- Choosing between modern ML-based causal estimators — including double machine learning
- SKILL.md covers When to Use This Skill, Where to Start, Causal ML vs Traditional Methods and Double Machine Learning (DML), plus 8 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Partially linear models
What it does
Causal ML is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. This skill covers causal machine learning methods in applied economics and quantitative social science. Use when implementing or choosing between modern ML-based causal estimators — including double machine learning, DML, partially linear models, interactive regression models, cross-fitting, Neyman orthogonality, debiased ML, causal forests, generalized random forest, GRF, honest causal trees, AIPW with machine learning, doubly robust with machine learning, DR-Learner, T-Learner, S-Learner, X-Learner…
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/connections-traditional.md`, `references/dml.md` and `references/grf-meta-learners.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
- Choosing between modern ML-based causal estimators — including double machine learning
- Partially linear models
- Interactive regression models
- Neyman orthogonality
Example prompts
- “/causal-ml”
Requirements
- Python 3
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.
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
Causal ML loads about 2.2k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 215 tokens; SKILL.md has 788 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 788 words (~2,171 tokens).
“Reference for semiparametric ML estimators: DML with cross-fitting, generalized random forests, debiased regularization, and nuisance function approximation. Covers Neyman-orthogonal moment conditions, sample splitting, plug-in bias correction, and heterogeneous treatment effects.”
- name
- causal-ml
- argument-hint
- <estimator or method choice>
Files
SKILL.md and 5 other files (references) in skills/11-James-Traina-compound-science/skills/causal-ml of brycewang-stanford/Auto-Empirical-Research-Skills.
- SKILL.md
- references/connections-traditional.md
- references/dml.md
- references/grf-meta-learners.md
- references/high-dim-cross-fitting.md
- references/hte-inference.md
Open the folder on GitHubat commit 9fa87d8
Compare with similar skills
Causal ML 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 |
|---|---|---|---|---|---|---|
| Causal ML this skillbrycewang-stanford/Auto-Empirical-Research-Skills | 4.5k | — | ~2.2k | Automated safety check: Pass | Custom licence | |
| Senior Data Scientistalirezarezvani/claude-skills | 28k | 1 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Senior Data Scientistborghei/Claude-Skills | 886 | — | ~1.7k | 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 Causal ML
What does Causal ML do?
This skill covers causal machine learning methods in applied economics and quantitative social science. Causal ML is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. This skill covers causal machine learning methods in applied economics and quantitative social science.
When should I use Causal ML?
Causal ML fits situations like: choosing between modern ML-based causal estimators — including double machine learning; partially linear models; interactive regression models; neyman orthogonality.
How do I install Causal ML in Claude Code?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-ml -a claude-code`. Or copy the skill folder (skills/11-James-Traina-compound-science/skills/causal-ml in brycewang-stanford/Auto-Empirical-Research-Skills) into .claude/skills/causal-ml in your project. Claude Code loads it when a task matches its description.
How do I install Causal ML in Codex?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-ml -a codex`. Or copy the skill folder (skills/11-James-Traina-compound-science/skills/causal-ml in brycewang-stanford/Auto-Empirical-Research-Skills) into .agents/skills/causal-ml in your project. Codex loads it when a task matches its description.
Can I use Causal ML 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 causal-ml -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/causal-ml, .gemini/skills/causal-ml, .github/skills/causal-ml and .opencode/skills/causal-ml in your project.
What does Causal ML need to run?
SKILL.md names no scripts, command-line tools or credentials: Causal ML is instructions for the agent only. Our summary lists: Python 3.
Does Causal ML 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 Causal ML 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 Causal ML use?
Causal ML 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 Causal ML use?
About 2.2k tokens (SKILL.md is roughly 8.7k 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 8.8k tokens, read only when the agent opens those files.
What are the alternatives to Causal ML?
Skills that share tags, products or a category with Causal ML: Senior Data Scientist (alirezarezvani/claude-skills, 28k stars), Senior Data Scientist (borghei/Claude-Skills, 886 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 Causal ML?
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.