ml4t-causal-identification skillA
ml4t-causal-identification is agent-read markdown (skill) from ml4t/skills: Validate causal claims using DAG adjustment sets, bad-control detection, and refutation tests. Use when distinguishing genuine factor effects from confounded associations..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Causal Identification
A factor with IC 0.04 could be a real effect or a confounded association. Without a DAG and refutation tests, you cannot tell which. Conditioning on the wrong variables - mediators, colliders, post-treatment - can create or destroy apparent signal.
## The Problem
"Kitchen sink regression" - conditioning on every available variable - is the default in ML pipelines. But including a collider (e.g., fund flows driven by both momentum and returns) induces spurious correlation (~-0.25 between independent variables). Including a mediator (the channel through which the treatment operates) attenuates the true effect. Including a post-treatment variable introduces bias of unknown sign. The DAG determines which variables are admissible controls.
## The Pattern
### WRONG
```python
import numpy as np
from sklearn.linear_model import Ridge
# Kitchen-sink: include everything as controls
# fund_flow is a COLLIDER (driven by both momentum and returns) - induces bias
X = np.column_stack([momentum, volatility, fund_flow, sector_return])
model = Ridge().fit(X, forward_returns)
print(f"Momentum coeff: {model.coef_[0]:.4f}") # Biased by collider conditioning
```
…Read the whole file at its exact version.
How to install
mdr add ml4t/skills/ml4t-causal-identification@git:20260901.c415df0mdr add ml4t/skills/ml4t-causal-identification@sha256:8f230ce7e691e93aPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_6c6cracvlxyddftj)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260901.c415df0 latest | 2026-09-01 | c415df0 | 5,760 B | A | view · diff |
| git:20260528.303089e | 2026-05-28 | 303089e | 5,801 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (5760 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
ml4t/skills · 19 stars · license Apache-2.0 · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_6c6cracvlxyddftj GET https://markdownregistry.com/api/v1/resolve?ref=ml4t/skills/ml4t-causal-identification GET https://markdownregistry.com/api/v1/blob/8f230ce7e691e93afc9b9643d2efe2cd827841a45f411edfb27e9d22dac02683
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