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causal-inference

Run causal-inference analyses from observational data using PyMC, CausalPy, and DoWhy.

161|18|Updated Mar 2, 2026
Search Tags:#dags#causal-inference#bayesian#pymc#dowhy#causalpy#refutation
One-click install
npx skills add https://github.com/Learning-Bayesian-Statistics/baygent-skills --skill causal-inference-learning-bayesian-statistics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: causal-inference
Source: https://github.com/Learning-Bayesian-Statistics/baygent-skills/tree/main/causal-inference
Command: npx skills add https://github.com/Learning-Bayesian-Statistics/baygent-skills --skill causal-inference-learning-bayesian-statistics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Production-grade Bayesian causal inference workflow to produce defensible causal estimates using PyMC, CausalPy, and DoWhy, with DAG-first design and built-in guardrails.

Core Features & Use Cases

  • End-to-end causal workflow: formulate questions, draw DAGs, identify strategies, estimate models, refute results, and report findings.
  • Integration with bayesian-workflow for priors, sampling, diagnostics, and full posterior uncertainty; supports DiD, IV, RDD, ITS, Synthetic Control, mediation, and counterfactuals.
  • Guardrails: mandatory assumption checkpoints, DoWhy-based identification, and reporting guardrails with careful language.

Quick Start

Install the bayesian-workflow and causal-inference skills, then load your DAG and data to run a DoWhy-based causal analysis.

Frequently Asked Questions about causal-inference

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run a causal inference analysis using observational data with PyMC and DoWhy?▼

Run causal inference by loading your observational data and DAG into the workflow to execute DoWhy-based identification, estimate models in PyMC, and apply refutation tooling for defensible causal estimates.

What is the best way to estimate difference-in-differences and synthetic control models using Bayesian methods?▼

Estimate DiD and Synthetic Control Models by applying CausalPy and PyMC within a Bayesian causal inference workflow, producing posterior uncertainty intervals for your causal estimates.

Does this causal inference workflow support instrumental variables and regression discontinuity designs?▼

Yes, the causal inference workflow supports Instrumental Variables, Regression Discontinuity Designs, Interrupted Time Series, mediation, and counterfactual queries across business and research contexts.

How do DAGs help identify causal effects before estimating models in DoWhy?▼

DAGs provide the graphical structure required for DoWhy-based causal identification, validating assumptions and isolating causal effects before model estimation through mandatory user assumption checkpoints.

Do I need the bayesian-workflow skill to perform causal analysis with this tool?▼

Yes, integrating the bayesian-workflow skill is required to define priors, run sampling diagnostics, and propagate full posterior uncertainty through your causal inference estimates.

What limitations should I watch for when refuting causal estimates from observational data?▼

Refuting causal estimates requires explicit assumption checkpoints and careful reporting language, as causal inference from observational data relies on untestable DAG assumptions that must be validated manually.

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