econml
- Repo stars 307
- License MIT
- Author updated Live
- Author repo zorai
- Domain
- Other · econml · causal-inference · heterogeneous-treatment-effects
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 100 / 100 · audit passed
- Author / version / license
- @mkurman · MIT
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
---
name: econml
description: EconML (Microsoft) — heterogeneous treatment effect estimation. Double ML, Causal Forest, Deep I…
category: other
runtime: Python
---
# econml output preview
## PART A: Task fit
- Use case: EconML (Microsoft) — heterogeneous treatment effect estimation. Double ML, Causal Forest, Deep IV, and metalearners (S-Learner, T-Learner, X-Learner). Orthogonal learning for causal effects from observational data..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Overview / Installation / Double ML (Linear)” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “EconML (Microsoft) — heterogeneous treatment effect estimation. Double ML, Causal Forest, Deep IV, and metalearners (S-Learner, T-Learner, X-Learner). Orthogonal learning for causal effects from observational data.”.
- **02** When the source has headings, the agent prioritizes “Overview / Installation / Double ML (Linear)” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “Overview / Installation / Double ML (Linear)”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: econml
description: EconML (Microsoft) — heterogeneous treatment effect estimation. Double ML, Causal Forest, Deep I…
category: other
source: mkurman/zorai
---
# econml
## When to use
- EconML (Microsoft) — heterogeneous treatment effect estimation. Double ML, Causal Forest, Deep IV, and metalearners (S…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Overview / Installation / Double ML (Linear)” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "econml" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Overview / Installation / Double ML (Linear)
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}Overview
EconML is a Microsoft library for causal inference and heterogeneous treatment effect estimation using machine learning. Implements Double ML, Causal Forest, DML, IV methods, and orthogonal statistical learning. Designed for observational data where treatment effects vary across individuals.
Installation
uv pip install econmlDouble ML (Linear)
from econml.dml import LinearDML
import numpy as np
X = np.random.randn(500, 5) # features
T = np.random.randn(500) # treatment
Y = T * (0.5 + X[:, 0]) + np.random.randn(500) # outcome
est = LinearDML(model_y="auto", model_t="auto", discrete_treatment=False)
est.fit(Y, T, X=X)
print(f"ATE: {est.ate():.3f} ± {est.ate_inference().stderr:.3f}")Causal Forest
from econml.grf import CausalForest
cf = CausalForest(n_estimators=100, min_samples_leaf=10)
cf.fit(X, T, Y)
treatment_effects = cf.effect(X)
print(f"Heterogeneous effects range: {treatment_effects.min():.3f} to {treatment_effects.max():.3f}")
Decide Fit First
Design Intent
How To Use It
Boundaries And Review