Individual skill
econml-causal-guide
Apply EconML for causal inference combining machine learning and econometrics
skills/analysis/econometrics/econml-causal-guide/SKILL.md
Source commit bf44b3cd617fa94c8a1b254c5d1987142ca3d631
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Instructions only
- Last checked
- Nov 5, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 310
- Repository last updated
- Jun 19, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- education Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
Read excerpt
# 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, public health, education policy, and any field where understanding causal mechanisms from non-experimental data is critical.
## Installation and Setup
Install EconML via pip:
