This skill covers causal inference methods in observational and quasi-experimental settings.

Custom licenceAuto-check passedResearch & Science

Install Causal Inference

skills CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills causal-inference --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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-inference .claude/skills/causal-inference && rm -rf skills-src

Use ~/.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/

Facts

Skill name
causal-inference
GitHub stars
4.5k
Token cost
~3.1k tokens
SKILL.md length
1,196 words
Files
4 (incl. references)
Skills in repo
383
Repo updated
First seen
Licence
Custom licence

At a glance

This skill covers causal inference methods in observational and quasi-experimental settings.

  • Works in 6 steps: Is there a sharp threshold? → RDD → Is there an instrument? → IV → Is there a clean pre/post +… → …
  • The user is implementing
  • SKILL.md covers When to Use This Skill, Where to Start, Frameworks and Quick Reference: Methods at a…, plus 10 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Causal Inference is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. This skill covers causal inference methods in observational and quasi-experimental settings. Use when the user is implementing, choosing between, or debugging causal identification strategies — including instrumental variables, difference-in-differences, regression discontinuity, synthetic control, or matching estimators. Triggers on "causal effect", "identification strategy", "instrumental variable", "2SLS", "GMM", "difference-in-differences", "DiD", "staggered treatment", "regression discontinuity", "RDD"…

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/method-implementations.md`, `references/staggered-did.md` and `references/synthetic-control.md`).

It sits in Research & Science, covering 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

  • The user is implementing
  • Choosing between
  • Debugging causal identification strategies — including instrumental variables
  • Difference-in-differences

Example prompts

  • “causal effect”
  • “identification strategy”
  • “instrumental variable”
  • “/causal-inference”

Requirements

  • Python 3

Workflow steps

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

  1. Is there a sharp threshold? → RDD
  2. Is there an instrument? → IV
  3. Is there a clean pre/post + treated/control? → DiD
  4. Only one treated unit? → Synthetic control
  5. Rich observables, selection on observables plausible? → AIPW
  6. None of the above → structural model may be needed

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 python).

    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 Inference loads about 3.1k tokens when it runs, and up to ~7.7k if it reads all its reference files. Until then it costs about 184 tokens; SKILL.md has 1,196 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~184
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.7k

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

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 1,196 words (~3,145 tokens).

“Reference for implementing causal inference methods: from identification strategy to estimation to diagnostics and robustness. Covers the major quasi-experimental and observational methods used in applied economics and quantitative social science.”

— opening of SKILL.md by brycewang-stanford, Custom licence
name
causal-inference
argument-hint
<method or identification strategy>

Read the full SKILL.md on GitHub

Files

SKILL.md and 3 other files (references) in skills/11-James-Traina-compound-science/skills/causal-inference of brycewang-stanford/Auto-Empirical-Research-Skills.

  • SKILL.md
  • references/method-implementations.md
  • references/staggered-did.md
  • references/synthetic-control.md

Open the folder on GitHubat commit 9fa87d8

Compare with similar skills

Causal Inference 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.

Causal Inference compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Causal Inference this skillbrycewang-stanford/Auto-Empirical-Research-Skills4.5k—~3.1kAutomated safety check: PassCustom licence
Statadylantmoore/stata-skill2911 repos~4.2kAutomated safety check: PassCustom licence
Stata C Pluginsdylantmoore/stata-skill2911 repos~5.8kAutomated safety check: PassCustom licence
Example Datasetspymc-labs/CausalPy1.2k—~587Automated safety check: PassApache-2.0
Stata AuditSepineTam/mcp-for-stata264—~1.2kAutomated safety check: PassAGPL-3.0
Stata Skill Contributordylantmoore/stata-skill2911 repos~2.4kAutomated safety check: PassCustom licence

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Questions about Causal Inference

What does Causal Inference do?

This skill covers causal inference methods in observational and quasi-experimental settings. Causal Inference is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. This skill covers causal inference methods in observational and quasi-experimental settings.

When should I use Causal Inference?

Causal Inference fits situations like: the user is implementing; choosing between; debugging causal identification strategies — including instrumental variables; difference-in-differences.

How do I install Causal Inference in Claude Code?

Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference -a claude-code`. Or copy the skill folder (skills/11-James-Traina-compound-science/skills/causal-inference in brycewang-stanford/Auto-Empirical-Research-Skills) into .claude/skills/causal-inference in your project. Claude Code loads it when a task matches its description.

How do I install Causal Inference in Codex?

Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill causal-inference -a codex`. Or copy the skill folder (skills/11-James-Traina-compound-science/skills/causal-inference in brycewang-stanford/Auto-Empirical-Research-Skills) into .agents/skills/causal-inference in your project. Codex loads it when a task matches its description.

Can I use Causal Inference 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-inference -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-inference, .gemini/skills/causal-inference, .github/skills/causal-inference and .opencode/skills/causal-inference in your project.

What does Causal Inference need to run?

SKILL.md names no scripts, command-line tools or credentials: Causal Inference is instructions for the agent only. Our summary lists: Python 3.

Does Causal Inference 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 Inference 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 Inference use?

Causal Inference 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 Inference use?

About 3.1k tokens (SKILL.md is roughly 13k 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 4.5k tokens, read only when the agent opens those files.

What are the alternatives to Causal Inference?

Skills that share tags, products or a category with Causal Inference: Stata (dylantmoore/stata-skill, 291 stars), Stata C Plugins (dylantmoore/stata-skill, 291 stars), Example Datasets (pymc-labs/CausalPy, 1.2k stars) and Stata Audit (SepineTam/mcp-for-stata, 264 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Causal Inference?

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.