causal-inference-engine

name	causal-inference-engine
description	Causal reasoning implementing DAG construction, do-calculus, and intervention effect estimation
allowed-tools	["Bash","Read","Write","Edit","Glob","Grep"]
metadata	{"specialization":"scientific-discovery","domain":"science","category":"hypothesis-reasoning","phase":6}
graph	{"domains":["domain:scientific-discovery"],"specializations":["specialization:scientific-research-methods"],"skillAreas":["skill-area:data-analysis","skill-area:statistical-analysis","skill-area:deep-web-research"],"workflows":["workflow:experiment-design","workflow:peer-review-cycle"],"roles":["role:research-engineer","role:computational-scientist"]}

Causal Inference Engine

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

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

Tools/Libraries

• DoWhy
• CausalNex
• pgmpy
• EconML