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causalis.scenarios.unconfoundedness.dgp

dgp

Submodule causalis.scenarios.unconfoundedness.dgp with no child pages and 4 documented members.

Symbol indexAPI membersobs_linear_26_datasetgenerate_obs_hte_26generate_obs_hte_26_richgenerate_obs_hte_binary_26

Functions

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4 items
obs_linear_26_datasetfunctiongenerate_obs_hte_26functiongenerate_obs_hte_26_richfunctiongenerate_obs_hte_binary_26function
function
causalis.scenarios.unconfoundedness.dgp.obs_linear_26_dataset

obs_linear_26_dataset

A pre-configured observational linear dataset with 5 standard confounders. Based on the scenario in docs/cases/dml_ate.ipynb.

Parameters

nint, default=10000

Number of samples.

seedint, default=42

Random seed.

include_oraclebool, default=True

Whether to include oracle ground-truth columns like ‘cate’, ‘propensity’, etc.

return_causal_databool, default=True

If True, returns a CausalData object. If False, returns a pandas DataFrame.

Returns

pandas.DataFrame or CausalData

Generated observational sample, optionally wrapped as CausalData.

Canonical target

causalis.scenarios.unconfoundedness.dgp.obs_linear_26_dataset

Sections

ParametersReturns
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function
causalis.scenarios.unconfoundedness.dgp.generate_obs_hte_26

generate_obs_hte_26

Observational dataset with nonlinear outcome model, nonlinear treatment assignment, and a heterogeneous (nonlinear) treatment effect tau(X). Based on the scenario in notebooks/cases/dml_atte.ipynb.

Parameters

nint, default=10000

Number of samples.

seedint, default=42

Random seed.

include_oraclebool, default=True

Whether to include oracle ground-truth columns like ‘cate’, ‘propensity’, etc.

return_causal_databool, default=True

If True, returns a CausalData object. If False, returns a pandas DataFrame.

Returns

pandas.DataFrame or CausalData

Generated heterogeneous-treatment-effect sample, optionally wrapped as CausalData.

Canonical target

causalis.scenarios.unconfoundedness.dgp.generate_obs_hte_26

Sections

ParametersReturns
Link to this symbol
function
causalis.scenarios.unconfoundedness.dgp.generate_obs_hte_26_rich

generate_obs_hte_26_rich

Observational dataset with richer confounding, nonlinear outcome model, nonlinear treatment assignment, and heterogeneous treatment effects. Adds additional realistic covariates and dependencies to mimic real data.

Parameters

nint, default=100000

Number of samples.

seedint, default=42

Random seed.

include_oraclebool, default=True

Whether to include oracle ground-truth columns like ‘cate’, ‘propensity’, etc.

return_causal_databool, default=True

If True, returns a CausalData object. If False, returns a pandas DataFrame.

Returns

pandas.DataFrame or CausalData

Generated rich observational sample, optionally wrapped as CausalData.

Canonical target

causalis.scenarios.unconfoundedness.dgp.generate_obs_hte_26_rich

Sections

ParametersReturns
Link to this symbol
function
causalis.scenarios.unconfoundedness.dgp.generate_obs_hte_binary_26

generate_obs_hte_binary_26

Observational binary-outcome dataset with nonlinear confounding and heterogeneous treatment effects.

This scenario follows the structure of generate_obs_hte_26_rich, but uses a binary outcome model and a modified confounder set.

Parameters

nint, default=100000

Number of samples.

seedint, default=42

Random seed.

include_oraclebool, default=True

Whether to include oracle columns like ‘cate’, ‘propensity’, etc.

return_causal_databool, default=True

If True, returns a CausalData object. If False, returns a pandas DataFrame.

Returns

pandas.DataFrame or CausalData

Generated binary-outcome sample, optionally wrapped as CausalData.

Canonical target

causalis.scenarios.unconfoundedness.dgp.generate_obs_hte_binary_26

Sections

ParametersReturns
Link to this symbol