causal-inference-ml
Apply causal inference methods for treatment effect estimation and uplift modeling
npx skills add majiayu000/claude-skill-registry --skill causal-inference-ml --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Causal Inference for ML ## Method Selection Decision Table | Scenario | Key Assumption | Method | Library | |----------|----------------|--------|---------| | RCT / A/B test | Randomization | Difference-in-means | scipy | | Observational, known confounders | Unconfoundedness | T/S/X-Learner, CausalForestDML | EconML | | Observational, high-dim confounders | Unconfoundedness + sparsity | DML (Double ML) | EconML | | Selection on observables | Strong ignorability | IPW / Hajek estimator | DoWhy | | Unobserved confounders, instrument exists | Valid instrument | IV / 2SLS / DMLIV | EconML | | Pre/post with control group | Parallel trends | Difference-in-Differences | statsmodels | | Full causal graph validation | Graph structure known | DoWhy (identify, estimate, refute) | DoWhy | | Targeting / personalization | Unconfoundedness | Uplift modeling + Qini curves | EconML | ## DoWhy Pipeline ```python from dowhy import CausalModel def run_dowhy_pipeline(df, treatment, outcome, confounders, instruments=None, effect_modifiers=None): """Full DoWhy pipeline: model, identify, estimate, refute.""" model = CausalModel( data=df, treatment=treatment, outcome=outcome, common_causes=confounders, i
- Method Selection Decision Table
- DoWhy Pipeline
- EconML Meta-Learners
- T-Learner, S-Learner, X-Learner
- CausalForestDML
- Propensity Scoring with IPW
- Instrumental Variables with DMLIV
- Difference-in-Differences
- Uplift Modeling with Qini Curves
- Gotchas
What does the causal-inference-ml skill do?
Apply causal inference methods for treatment effect estimation and uplift modeling
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill causal-inference-ml --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.