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databricks-industry-solutionsdatabricks-industry-solutions/hls-skills/skills/rwe-cohortstudy/SKILL.md

rwe-cohortstudy

Skill6 starsChanged 20 days agoLicence not identifiedView on GitHub

Conduct real-world evidence (RWE) comparative effectiveness studies in Python. Covers propensity score matching, inverse probability weighting, overlap weighting, doubly robust estimation, balance diagnostics (SMD, love plots), treatment effect estimation, survival analysis, sensitivity analysis (E-values, tipping points), external control arm integration, marginal structural models for time-varying confounding, and target trial emulation. Read this skill when the user asks to compare treatment groups in observational data, run propensity score analysis, build external control arms, emulate a target trial, perform causal inference from EHR/claims data, or conduct comparative cohort studies with confounding adjustment.

  • Installs packages

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

The licence could not be identified — read it at the source. We count its rules but don't quote them. Read them on GitHub.

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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