Estimate intervention effects from existing data using causal analysis methods. Set up models, compare observed outcomes with estimates of what would have happened without intervention, check robustness, and interpret fitted results.
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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.
实测评分 未实测
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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.
summary(), print_coefficients(), and plot().experiment.summary(): Prints model summary and main results.experiment.plot(): Visualizes observed vs. counterfactual.experiment.print_coefficients(): Shows model coefficients.Detailed usage for specific methods:
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Support experimental validation of designed proteins through a cloud laboratory. Prepare and optimize sequences, submit tests for binding, expression, thermal stability, or enzyme activity, track experiment progress, and retrieve results through the API.
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Analyze time series, observations ordered over time, using aeon in Python. Build classification, prediction, and clustering workflows, detect unusual patterns or changes, and compare methods through an interface compatible with scikit-learn.
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Create original generative art with p5.js, using code to produce patterns and motion. Receive a written artistic concept and an interactive HTML viewer with adjustable parameters, reproducible random variations, seed navigation, and PNG downloads.
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Retrieve stock prices, exchange rates, cryptocurrency prices, company financial statements, and economic indicators through the Alpha Vantage API. It also covers historical data, news sentiment, and technical indicators, which summarize price and trading patterns.
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Design backend systems with clear boundaries between business rules, databases, and external services. Use Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to organize new applications, refactor existing code, and make core logic easier to test.
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Helps process astronomical data in Python by converting coordinates, units, and time scales, working with FITS observation files, matching object catalogs, and calculating distances using cosmological models.
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