DoWhy

Use this repo skill for the dowhy Python package. It is a router for the package's main user-facing workflows, not a full manual.

Start here

If the user names the package but not a specific workflow, read references/package-overview.md first. It maps the public modules to the right sub-skill.

Route map

• Classic causal effect estimation, identification, refutation, or CausalModel workflows belong in sub-skills/effect-estimation/SKILL.md.
• Graphical causal models, mechanisms, interventions, counterfactuals, anomaly attribution, distribution change, and validation belong in sub-skills/graphical-causal-models/SKILL.md.
• Graph parsing, graph/data alignment, pandas .causal.do, do-samplers, datasets, plotting backends, and temporal helpers belong in sub-skills/data-graph-interfaces/SKILL.md.
• Optional integrations and boundary features such as causal prediction, TabPFN, EconML/CausalML wrappers, graph discovery wrappers, pydot, and pygraphviz belong in references/optional-integrations.md.
• Cross-cutting install/import and workflow confusion goes to references/troubleshooting.md.

Install and quick smoke check

The public package install is:

python -m pip install dowhy

If the user needs plotting, graph parsing, or optional estimators, read references/optional-integrations.md first because some extras are version-sensitive or require external packages.

A quick runtime sanity check is:

python -c "import dowhy, dowhy.gcm; from dowhy import CausalModel; print(dowhy.__version__)"

For a broader environment probe, run:

python scripts/check_dowhy_environment.py

If you are working on a local checkout rather than using a published wheel, install from the checkout with an editable install before editing the package, but keep that contributor workflow separate from the runtime guidance here.

How to choose the right path

1. If the user wants an effect estimate or robustness check, stay in effect-estimation.
2. If the user wants generated samples, interventions, counterfactuals, or causal influence from a fitted graph model, use graphical-causal-models.
3. If the user first needs to build, parse, or align a graph and DataFrame, use data-graph-interfaces and then route downstream.
4. If the user is asking about an optional dependency, treat it as a boundary question, not a core workflow failure, unless the task explicitly requires that optional package.
5. If the user only wants to know whether this skill matches their checkout, read references/repo-provenance.md.

What this skill covers well

• CausalModel construction, identification, estimation, do, and refutation.
• dowhy.gcm model classes, mechanism assignment, sampling, attribution, and validation.
• Graph parsing and graph/DataFrame setup needed by both core workflows.
• Optional integration boundaries and troubleshooting for missing extras.

What this skill does not try to do by itself

• It does not replace the sub-skills above with one giant manual.
• It does not claim optional integrations are always installed.
• It does not require the original checkout once the bundled skill files exist.
• It does not describe repo test or review artifacts; those live under skills/tests/dowhy/.

Reference map

• references/package-overview.md for the module-to-workflow map.
• references/optional-integrations.md for optional packages, extras, and boundary features.
• references/troubleshooting.md for the most common cross-cutting failures and the best next check.
• references/repo-provenance.md when checking whether this skill is current for the present checkout.
• references/repo-routing-metadata.json for router metadata used by the managed repo-skill importer.
• scripts/check_dowhy_environment.py for a small environment probe that reports core and optional imports.

Response style

When routing a user question, name the sub-skill, state the core package object or workflow family, and give one validation or troubleshooting check. If the question is about an optional integration, say so explicitly and mention the likely dependency boundary.