causalis.data_contracts
Modules
- causal_diagnostic_data –
- causal_estimate –
- causaldata – Causalis Dataclass for storing Cross-sectional DataFrame and column metadata for causal inference.
- causaldata_instrumental –
- multicausal_estimate –
- multicausaldata – Causalis Dataclass for storing Cross-sectional DataFrame and column metadata
- regression_checks –
Classes
- CausalData – Container for causal inference datasets.
- CausalDataInstrumental – Container for causal inference datasets with causaldata_instrumental variables.
- CausalDatasetGenerator – Generate synthetic causal inference datasets with controllable confounding,
- CausalEstimate – Result container for causal effect estimates.
- DiagnosticData – Base class for all diagnostic data_contracts.
- MultiCausalData – Data contract for cross-sectional causal data with multi-class one-hot treatments.
- RegressionChecks – Lightweight OLS/regression health checks for CUPED diagnostics.
- UnconfoundednessDiagnosticData – Fields common to all models assuming unconfoundedness.
Functions
- classic_rct_gamma – Generate a classic RCT dataset with three binary confounders and a gamma outcome.
- classic_rct_gamma_26 – A pre-configured classic RCT dataset with a gamma outcome.
- generate_classic_rct – Generate a classic RCT dataset with three binary confounders:
- generate_classic_rct_26 – A pre-configured classic RCT dataset with 3 binary confounders.
- generate_cuped_binary – Binary CUPED-oriented DGP with richer confounders and structured HTE.
- generate_rct – Generate an RCT dataset with randomized treatment assignment.
- make_cuped_binary_26 – Binary CUPED benchmark with richer confounders and structured HTE.
- make_gold_linear – A standard linear benchmark with moderate confounding.
- obs_linear_26_dataset – A pre-configured observational linear dataset with 5 standard confounders.
- obs_linear_effect – Generate an observational dataset with linear effects of confounders and a constant treatment effect.