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genpark-backdoor-criterion-confounder-adjustment-skill

GenPark AI Agent Skill - Graph-theoretic backdoor criterion validator and minimal confounder adjustment set identifier ensuring unconfounded causal effect estimation.

Details

External ID
1362860743
Source
GITHUB
Company
—
Product
genpark-backdoor-criterion-confounder-adjustment-skill
Website domain
github.com
Launched
Sept. 9, 2026
Cohort
—
Upvotes
8
Upvotes percentile
0.14514476044068664
Tags
agentic-ai, causal-inference, counterfactuals, do-calculus, genpark-skill, instrumental-variables, mcp, propensity-score, python-stdlib, structural-causal-models
Fetched at
Sept. 13, 2026, 5:57 p.m.
Updated at
Sept. 13, 2026, 5:57 p.m.

Enrichment

Theme
specialized AI models and agent reasoning tools
Vertical
Horizontal
Function
Agent / copilot
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
graph-theoretic backdoor criterion validator skill for ai agents
Manually corrected
False

Could you build this?

Yes Evaluating the backdoor criterion and identifying minimal adjustment sets on DAGs relies on standard d-separation graph algorithms well within LLM coding abilities.

Competitors

Other products that read as similar to this one — 1218 launches clear the similarity bar, closest 8 shown.

Attention rank: #659 of 1219 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 314 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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