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running-causalpy-experiments

separate

Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.

When to use

• preparing data
• instantiating the experiment
• choosing a model backend
• setting sane priors
• inspecting outputs
• plotting
• communicating results
• running sensitivity checks

When not to use

• selecting the causal method

Skill page

Source