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causal-ml

This skill covers causal machine learning methods in applied economics and quantitative social science.

Skill by brycewang-stanford

IndexedNOASSERTION★ 4,757
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Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/brycewang-stanford-causal-ml

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

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, meta-learners, heterogeneous treatment effects, conditional average treatment effect, CATE, HTE, high-dimensional controls, LASSO controls, post-LASSO, post-double selection, Belloni-Chernozhukov-Hansen, Riesz representer, Chernozhukov, sample splitting, econml, DoubleML package, or any combination of machine learning and causal inference.

Use cases: ml

Reviews

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/causal-ml

What's inside

Skill name
causal-ml
Description
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, meta-learners, heterogeneous treatment effects, conditional average treatment effect, CATE, HTE, high-dimensional controls, LASSO controls, post-LASSO, post-double selection, Belloni-Chernozhukov-Hansen, Riesz representer, Chernozhukov, sample splitting, econml, DoubleML package, or any combination of machine learning and causal inference.
License
NOASSERTION
Instructions
132 lines in SKILL.md · view on GitHub

Files (6)

  • SKILL.md
  • connections-traditional.md · references/
  • dml.md · references/
  • grf-meta-learners.md · references/
  • high-dim-cross-fitting.md · references/
  • hte-inference.md · references/

The license isn't open source, so the instructions aren't shown here. Read SKILL.md on GitHub

More from brycewang-stanford/Auto-Empirical-Research-Skills

Signals

Stars
4,757
Forks
556
Open issues
1
Last commit (this folder)
Apr 3, 2026 (6 months ago)
Last release
Jul 2, 2026 (3 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
NOASSERTION
Repository
github.com/brycewang-stanford/Auto-Empirical-Research-Skills
Website
copaper.ai
First seen
Oct 24, 2026
Last checked
Oct 24, 2026
Source
GitHubgithub-graphql

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Cite this page

causal-ml skill. Agent Mart. https://agentmart.dev/skills/brycewang-stanford-causal-ml (last updated 2026-10-24)