Causal Graph Modeling Expert

Expert in causal graph modeling using Directed Acyclic Graphs (DAGs), structural causal models, do-calculus, and causal discovery algorithms. Guides practitioners through identification strategies, quasi-experimental designs, and implementation with DoWhy, CausalNex, and causal-learn.

Gold

v1.0.00 activationsData & AnalyticsTechnologyexpert

SupaScore

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.5

Completeness (10%)

8.5

User Satisfaction (20%)

Decision Usefulness (15%)

8.5

Best for

▸Identifying causal relationships between marketing campaigns and revenue using DAG analysis
▸Implementing backdoor criterion adjustment for unbiased treatment effect estimation
▸Building causal discovery pipelines with NOTEARS and PC algorithms in causal-learn
▸Designing difference-in-differences studies for policy impact evaluation
▸Creating sensitivity analysis frameworks for unobserved confounding in observational studies

What you'll get

●Hand-drawn DAG with d-separation analysis, Python code implementing backdoor adjustment in DoWhy, and sensitivity bounds for unmeasured confounding
●Causal discovery pipeline using PC algorithm with conditional independence tests, followed by structural equation modeling validation
●Complete difference-in-differences design with parallel trends testing, event study plots, and robustness checks across multiple specifications

Not designed for ↓

×Pure predictive modeling without causal claims
×Correlation analysis or statistical association studies
×Time series forecasting without causal interpretation
×A/B testing with perfect randomization (no confounding)

Expects

A clear causal question, domain knowledge about variable relationships, and observational or quasi-experimental data with potential confounders.

Returns

Causal DAGs, identification strategies, implementation code in DoWhy/CausalNex, and sensitivity analysis results with explicit assumptions.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

causal-inferencedagstructural-causal-modelsdo-calculusbackdoor-criterionfrontdoor-criterioncausal-discoverydowhycausalnexdifference-in-differencesregression-discontinuitysynthetic-controlinstrumental-variablesnotears

Research Foundation: 8 sources (3 books, 2 official docs, 3 academic)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial release

Prerequisites

Use these skills first for best results.

Python Data AnalystGold

Works well with

A/B Test AnalystPlatinum Bayesian Statistics ExpertPlatinum Experimental Design SpecialistGold Hypothesis Testing ExpertGold Regression Analysis ExpertPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Policy Impact EvaluatorGold Statistical Analysis AdvisorPlatinum

Common Workflows

Marketing Attribution Analysis

Build causal DAG for marketing channels, implement attribution modeling with proper confounding control, then design experiments to validate causal claims

causal-graph-modeling-expert→marketing-attribution-analyst→A/B Test Analyst

Activate this skill in Claude Code

Start Free to Activate This Skill