claude

causal-ts-discovery

Discover causal structure in nonstationary time series with causal-ts — data-health inspection, algorithm/CI-test selection, discovery, and graph interpretation.

115 on GitHubby Causal-TS authorsv0.28.0GPL-3.0-or-laterupdated todaydata-science
/

Plugin causal-ts-discovery from marketplace causal-ts in bloomberg/causal-ts. Commands read from the repo's manifest — today.

What it installs

Skills (1)

  • causal-ts-discovery

    Discover causal structure in time-series data with the causal-ts library. Use when the user wants a causal graph, or lead-lag / temporal dependencies, from time-series, sensor, or sequential data. NOT for effect size, ATE, or counterfactuals — that is effect estimation (see the handoff at the end).

View source on GitHub causal-ts.readthedocs.io

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