performing-causal-analysis
Fits causal models, estimates impacts, and plots results using CausalPy. Use when performing analysis with DiD, ITS, SC, or RD.
Best use case
performing-causal-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Fits causal models, estimates impacts, and plots results using CausalPy. Use when performing analysis with DiD, ITS, SC, or RD.
Teams using performing-causal-analysis should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/performing-causal-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How performing-causal-analysis Compares
| Feature / Agent | performing-causal-analysis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Fits causal models, estimates impacts, and plots results using CausalPy. Use when performing analysis with DiD, ITS, SC, or RD.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Performing Causal Analysis Executes causal analysis using CausalPy experiment classes. ## 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)
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