causal-design
FeaturedDesign or audit the identification strategy for an observational study. Use when the task concerns estimands, causal assumptions, threats to identification, or defensible research design rather than model implementation.
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Quality Score: 93/100
Skill Content
Details
- Author
- flonat
- Repository
- flonat/flonat-research
- Created
- 7 months ago
- Last Updated
- 3 days ago
- Language
- Python
- License
- MIT
Similar Skills
Semantically similar based on skill content — not just same category
causal-design
Triage a causal question to the identification strategy the data can support, then hand off to the owning method skill. Owns the selection-on-observables branch (overlap, doubly robust estimation, double ML, causal forests, policy learning, sensitivity analysis), plain panel fixed effects, and the inference rules shared across designs (clustering, multiplicity, interference routing). TRIGGER on "identification strategy", "which causal method", "research design", "endogeneity", "quasi-experiment", "natural experiment", "selection on observables", "unconfoundedness", "propensity score", "doubly robust", "double machine learning", "causal forest", "policy learning", "sensitivity analysis", "Oster bounds", "overlap", "panel fixed effects", "strict exogeneity", "surrogate index", "mediation", or "how do I estimate the effect of X on Y" with no design chosen yet. Once a design is named, its method skill owns it.
causal-inference-analysis
Causal effects: identification strategy, assumptions, estimators, robustness, claim bounds.
causal-audit
Correlation walked in; make it prove causation. Use when someone claims "X causes Y" from observational data, asks "did the change actually cause the improvement?", "is this confounded?", "what would have happened if we hadn't shipped it?", plans an A/B test, or investigates why an incident/outbreak spread the way it did.