Causal Inference Engine

SkillAI & models

Causal reasoning implementing DAG construction, do-calculus, and intervention effect estimation

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Causal Inference Engine skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/science/scientific-discovery/skills/causal-inference-engine/SKILL.md and read by ahel’s review.

Purpose

Provides causal reasoning capabilities implementing DAG construction, do-calculus, and intervention effect estimation.

Capabilities

Causal DAG construction and validation
Backdoor/frontdoor criterion checking
Average treatment effect estimation
Instrumental variable analysis
Mediation analysis
Sensitivity analysis for unmeasured confounding

Usage Guidelines

DAG Construction: Build causal graphs from domain knowledge
Identification: Check if effects are identifiable
Estimation: Apply appropriate estimation methods
Sensitivity: Assess robustness to unmeasured confounding

Tools/Libraries

DoWhy
CausalNex
pgmpy
EconML

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: causal-inference-engine
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter