econml

Other Community

Repo stars 307
License MIT
Author repo zorai

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: EconML (Microsoft) — heterogeneous treatment effect estimation. Double ML, Causal Forest, Deep IV, and metalearners (S-Learner…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Overview”, “Installation”, “Double ML (Linear)”, “Causal Forest”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name econml directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Overview / Installation / Double ML (Linear)” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

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 econml

Double 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}")

References

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Other · econml · causal-inference · heterogeneous-treatment-effects
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 100 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @mkurman · MIT
Fluxly token estimate: Lean
Fluxly setup estimate: Plug-and-play
External API key: No requirement detected
Detected OS requirements: Unspecified
Runtime requirements: Python
Detected file/system behavior: Read-only
Detected network behavior: Local-only
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Output preview econml.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

## 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

```bash
uv pip install econml
```

## Double ML (Linear)

```python
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

```python
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}")
```

## References
- [EconML docs](https://econml.azurewebsites.net/)
- [EconML GitHub](https://github.com/py-why/EconML)

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Overview → Installation → Double ML (Linear) → Causal Forest → References
terms    -> EconML is a Microsoft library for causal inference and heterogeneous treatment effect estimation using machine learning.
files/cmd -> github.com/py-why/EconML
body sha256 -> ffc705ea4493