---

name: causal-identification-and-effects description: "Guides pgmpy causal graph role annotations, identification, do-queries, ATE, and causal effect regressors." disable-model-invocation: true metadata: disco-role: operating license: MIT

Causal Identification and Effects

Use this sub-skill when the user asks for causal, interventional, or treatment-effect work in pgmpy: role-aware causal graphs, adjustment/frontdoor identification, CausalInference.query(..., do=...), CausalInference.estimate_ate, or sklearn-style causal effect regressors from pgmpy.prediction.

Route First

• Identify before estimating. Start with a graph carrying at least exposures and outcomes; identify or validate adjustment/frontdoor roles before making effect claims.
• For P(Y | do(X=x)), ATE, adjustment, frontdoor, IV, or DML: stay here and read references/workflows.md.
• For ordinary posterior P(Y | X=x) without intervention: route to the sibling inference/sampling skill instead of using causal terminology.
• For discovering the graph from data: route to the sibling structure-learning skill, then return here after the graph is accepted.
• For basic CPDs, factors, or Bayesian-network construction: route to the sibling modeling/factors skill.

What to Read

• references/causal-api.md — role names, supported classes, signatures, estimator inputs, and data assumptions.
• references/workflows.md — copyable recipes for adjustment, frontdoor, do-queries, ATE, and pgmpy.prediction regressors.
• references/troubleshooting.md — symptoms and fixes for missing roles, non-identifiability, data-column mismatches, do/evidence confusion, numeric/categorical issues, and IV role mistakes.
• scripts/causal_effect_smoke.py — safe installed-package smoke test for adjustment identification, frontdoor identification, ATE, and a causal regressor.

Operating Checklist

1. Confirm the requested estimand: observational posterior, interventional distribution, ATE, CATE-style prediction, or IV-style effect estimation.
2. Confirm graph roles and graph type. Adjustment needs exposures and outcomes; NaiveIVRegressor specifically needs singular role instrument.
3. Run identification/validation (Adjustment or Frontdoor) before fitting regressors or reporting effect estimates.
4. Confirm data columns exactly match role variable names and that regressor inputs are numeric.
5. Validate with the smoke script when adapting a new environment or when role/data mismatches are suspected:

python sub-skills/causal-identification-and-effects/scripts/causal_effect_smoke.py --help
python sub-skills/causal-identification-and-effects/scripts/causal_effect_smoke.py

Guardrails

• Do not claim causal identification from data alone; pgmpy's effect APIs assume the supplied causal graph is defensible.
• Do not treat evidence={"X": x} as do={"X": x}. Evidence conditions on observations; do intervenes.
• Do not use ordinary predictive accuracy as proof of causal validity. It can verify code execution, not the causal assumptions.
• Do not use deprecated legacy backdoor/frontdoor helpers when the pgmpy.identification.Adjustment or Frontdoor API covers the task.