econml-causal-guide
Apply EconML for causal inference combining machine learning and econometrics
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill econml-causal-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# EconML Causal Inference Guide ## Overview EconML is a Python package developed by Microsoft Research as part of the ALICE (Automated Learning and Intelligence for Causation and Economics) project. It provides a comprehensive suite of methods for estimating heterogeneous treatment effects from observational data, bridging the gap between modern machine learning and classical econometric techniques for causal inference. Traditional econometric approaches to causal inference often rely on strong parametric assumptions and struggle with high-dimensional data. Pure machine learning methods excel at prediction but do not inherently distinguish correlation from causation. EconML combines the strengths of both paradigms, offering methods that leverage the flexibility of ML for nuisance parameter estimation while maintaining the rigorous causal identification guarantees of econometric theory. The library implements cutting-edge methods from the academic literature including Double Machine Learning (DML), Causal Forests, Doubly Robust Learners, Orthogonal Random Forests, and Instrumental Variable methods with ML first stages. These tools are essential for researchers across economics, publ
- Overview
- Installation and Setup
- Core Estimators and Methods
- Research Workflow Integration
- Best Practices for Academic Research
- References
pip install econml pip install econml[all]
What does the econml-causal-guide skill do?
Apply EconML for causal inference combining machine learning and econometrics
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill econml-causal-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.