/performing-causal-analysis
Estimate causal effects from existing data. Use when fitting or interpreting DiD, ITS, synthetic control, regression discontinuity, or other treatment-effect analyses, including robustness checks and counterfactual plots. For choosing a study design before analysis, use
$ npx -y skills add foryourhealth111-pixel/Vibe-Skills --skill performing-causal-analysis --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/performing-causal-analysis
Context preview
The summary Claude sees to decide when to auto-load this skill.
Estimate causal effects from existing data. Use when fitting or interpreting DiD, ITS, synthetic control, regression discontinuity, or other treatment-effect analyses, including robustness checks and counterfactual plots. For choosing a study design before analysis, use
SKILL.md
performing-causal-analysis.SKILL.mdname: performing-causal-analysis
description: Estimate causal effects from existing data. Use when fitting or interpreting DiD, ITS, synthetic control, regression discontinuity, or other treatment-effect analyses, including robustness checks and counterfactual plots. For choosing a study design before analysis, use designing-experiments instead.
Performing Causal Analysis
Executes causal analysis on existing data. This skill owns model setup, treatment-effect estimation, counterfactual comparison, robustness checks, and interpretation of fitted causal results.
It does not own the earlier question of which experiment or quasi-experiment should be designed before analysis begins.
Workflow
1. **Load Data**: Ensure data is in a Pandas DataFrame. 2. **Initialize Experiment**: Use the appropriate class (see References). 3. **Fit & Model**: Models are fitted automatically upon initialization if arguments are provided. 4. **Analyze Results**: Use `summary()`, `print_coefficients()`, and `plot()`.
Core Methods
- `experiment.summary()`: Prints model summary and main results.
- `experiment.plot()`: Visualizes observed vs. counterfactual.
- `experiment.print_coefficients()`: Shows model coefficients.
References
Detailed usage for specific methods:
- [Difference-in-Differences](reference/diff_in_diff.md)
- [Interrupted Time Series](reference/interrupted_time_series.md)
- [Synthetic Control](reference/synthetic_control.md)
Read more
name: performing-causal-analysis description: Estimate causal effects from existing data. Use when fitting or interpreting DiD, ITS, synthetic control, regression discontinuity, or other treatment-effect analyses, including robustness checks and counterfactual plots. For choosing a study design before analysis, use designing-experiments instead.
Performing Causal Analysis
Executes causal analysis on existing data. This skill owns model setup, treatment-effect estimation, counterfactual comparison, robustness checks, and interpretation of fitted causal results.
It does not own the earlier question of which experiment or quasi-experiment should be designed before analysis begins.
Workflow
1. **Load Data**: Ensure data is in a Pandas DataFrame. 2. **Initialize Experiment**: Use the appropriate class (see References). 3. **Fit & Model**: Models are fitted automatically upon initialization if arguments are provided. 4. **Analyze Results**: Use `summary()`, `print_coefficients()`, and `plot()`.
Core Methods
- `experiment.summary()`: Prints model summary and main results.
- `experiment.plot()`: Visualizes observed vs. counterfactual.
- `experiment.print_coefficients()`: Shows model coefficients.
References
Detailed usage for specific methods:
- [Difference-in-Differences](reference/diff_in_diff.md)
- [Interrupted Time Series](reference/interrupted_time_series.md)
- [Synthetic Control](reference/synthetic_control.md)
VibeSkills is a general-purpose Skill that automatically routes local Skills and intelligently orchestrates harness workflows.
Repo: foryourhealth111-pixel/Vibe-Skills
Other skills on vibe-skills.
- /LQF_Machine_Learning_Expert_Guide
LQF Machine Learning Expert Guide - Routed skill for ML/Statistical Modeling with Critical Discussion Mode. Triggers on: machine learning, modeling, prediction, training, classification, regression, clustering, deep learning, neural network, model evaluation, feature
Open skill - /adaptyv
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use
Open skill - /aeon
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations
Open skill - /algorithmic-art
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing
Open skill - /alpha-vantage
Access real-time and historical stock market data, forex rates, cryptocurrency prices, commodities, economic indicators, and 50+ technical indicators via the Alpha Vantage API. Use when fetching stock prices (OHLCV), company fundamentals (income statement, balance sheet, cash
Open skill - /architecture-patterns
Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
Open skill