class
causalis.scenarios.classic_rct.model.DiffInMeansDiffInMeans
Difference-in-means model for CausalData. Wraps common RCT inference methods: t-test, bootstrap, and conversion z-test.
Python Docs
causalis
Reference
Package entry
causalisRoot package overview and namespace mapNamespaces
Submodule
causalis.scenarios.classic_rct.modelmodel
Submodule causalis.scenarios.classic_rct.model with no child pages and 4 documented members.
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method
causalis.scenarios.classic_rct.model.DiffInMeans.fitfit
Fit the model by storing the CausalData object.
Parameters
- dataCausalData
The CausalData object containing treatment and outcome variables.
Returns
DiffInMeans
The fitted model.
Canonical target
causalis.scenarios.classic_rct.model.DiffInMeans.fit
Sections
ParametersReturns
method
causalis.scenarios.classic_rct.model.DiffInMeans.estimateestimate
Compute the treatment effect using the specified method.
Parameters
- method{“ttest”, “bootstrap”, “conversion_ztest”}, default “ttest”
The inference method to use. - “ttest”: Standard independent two-sample t-test. - “bootstrap”: Bootstrap-based inference for difference in means. - “conversion_ztest”: Two-proportion z-test for binary outcomes.
- alphafloat, default 0.05
The significance level for calculating confidence intervals.
- diagnostic_databool, default True
Whether to include diagnostic data_contracts in the result.
- **kwargsAny
Additional arguments passed to the underlying inference function. - For “bootstrap”: can pass
n_simul,batch_size,seed,index_dtype.
Returns
CausalEstimate
A results object containing effect estimates and inference.
Canonical target
causalis.scenarios.classic_rct.model.DiffInMeans.estimate
Sections
ParametersReturns
method
causalis.scenarios.classic_rct.model.DiffInMeans.__repr__