identification-theory
CommunityClarify causal questions with formal IDs.
Education & Research#backdoor#DAG#potential-outcomes#mediation-analysis#frontdoor#causal-identification
AuthorData-Wise
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Identification Theory helps researchers determine when causal effects can be identified from observed data, and provides a formal language to derive identifiable expressions under specified assumptions.
Core Features & Use Cases
- Identify estimands and assumptions for causal effects using DAGs and potential outcomes.
- Derive identification formulas via back-door, front-door, and IV strategies, including mediation analysis.
- Model real-world problems with DAGs to decide identifiability and study design.
Quick Start
Draw a causal diagram for your variables A and Y and identify potential confounders. Specify the target estimand (e.g., E[Y(a)]) and the data available. Determine an identification strategy (back-door, front-door, or IV) and compute the estimand from observed data.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: identification-theory Download link: https://github.com/Data-Wise/scholar/archive/main.zip#identification-theory Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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