Index _ | A | B | C | D | E | F | G | I | K | M | O | P | R | S | T | U | X _ __init__() (causalkit.data.causaldata.CausalData method), [1], [2] (causalkit.data.generators.CausalDatasetGenerator method) (causalkit.eda.CausalDataLite method), [1] (causalkit.eda.CausalEDA method), [1] (causalkit.eda.eda.CausalDataLite method), [1] (causalkit.eda.eda.CausalEDA method) (causalkit.eda.eda.OutcomeModel method) (causalkit.eda.eda.PropensityModel method) __repr__() (causalkit.data.causaldata.CausalData method), [1] A aipw_score_ate() (in module causalkit.refutation.orthogonality) aipw_score_att() (in module causalkit.refutation.orthogonality) alpha_t (causalkit.data.generators.CausalDatasetGenerator attribute) alpha_y (causalkit.data.generators.CausalDatasetGenerator attribute) B beta_t (causalkit.data.generators.CausalDatasetGenerator attribute) beta_y (causalkit.data.generators.CausalDatasetGenerator attribute) bootstrap_diff_means() (in module causalkit.inference) C calculate_mde() (in module causalkit.design.mde) cate_esimand() (in module causalkit.inference) (in module causalkit.inference.cate.cate_esimand) CausalData (class in causalkit.data.causaldata), [1] CausalDataLite (class in causalkit.eda) (class in causalkit.eda.eda), [1] CausalDatasetGenerator (class in causalkit.data.generators) CausalEDA (class in causalkit.eda) (class in causalkit.eda.eda) causalforestdml() (in module causalkit.inference) causalkit.data.generators module causalkit.design.mde module causalkit.design.traffic_splitter module causalkit.eda.eda module causalkit.inference.ate.dml_ate module causalkit.inference.att.conversion_z_test module causalkit.inference.att.dml_att module causalkit.inference.att.ttest module causalkit.inference.cate.cate_esimand module causalkit.inference.gate.gate_esimand module causalkit.refutation.orthogonality module causalkit.refutation.placebo module causalkit.refutation.sensitivity module confounder_specs (causalkit.data.generators.CausalDatasetGenerator attribute) confounders (causalkit.data.causaldata.CausalData attribute), [1] (causalkit.data.causaldata.CausalData property), [1] (causalkit.eda.CausalDataLite attribute) (causalkit.eda.eda.CausalDataLite attribute), [1] confounders_means() (causalkit.eda.CausalEDA method) (causalkit.eda.eda.CausalEDA method), [1] confounders_roc_auc() (causalkit.eda.CausalEDA method) (causalkit.eda.eda.CausalEDA method), [1] conversion_z_test() (in module causalkit.inference) (in module causalkit.inference.att.conversion_z_test) D data_shape() (causalkit.eda.CausalEDA method) (causalkit.eda.eda.CausalEDA method), [1] df (causalkit.data.causaldata.CausalData attribute), [1] (causalkit.eda.CausalDataLite attribute) (causalkit.eda.eda.CausalDataLite attribute), [1] dml() (in module causalkit.inference) dml_ate() (in module causalkit.inference.ate.dml_ate) dml_att() (in module causalkit.inference) (in module causalkit.inference.att.dml_att) E extract_nuisances() (in module causalkit.refutation.orthogonality) F fit_propensity() (causalkit.eda.CausalEDA method) (causalkit.eda.eda.CausalEDA method), [1] G g_t (causalkit.data.generators.CausalDatasetGenerator attribute) g_y (causalkit.data.generators.CausalDatasetGenerator attribute) gate_esimand() (in module causalkit.inference) (in module causalkit.inference.gate.gate_esimand) generate() (causalkit.data.generators.CausalDatasetGenerator method) generate_rct_data() (in module causalkit.data.generators), [1] get_df() (causalkit.data.causaldata.CausalData method), [1] get_sensitivity_summary() (in module causalkit.refutation) (in module causalkit.refutation.sensitivity) I influence_summary() (in module causalkit.refutation.orthogonality) K k (causalkit.data.generators.CausalDatasetGenerator attribute) M module causalkit.data.generators causalkit.design.mde causalkit.design.traffic_splitter causalkit.eda.eda causalkit.inference.ate.dml_ate causalkit.inference.att.conversion_z_test causalkit.inference.att.dml_att causalkit.inference.att.ttest causalkit.inference.cate.cate_esimand causalkit.inference.gate.gate_esimand causalkit.refutation.orthogonality causalkit.refutation.placebo causalkit.refutation.sensitivity O oos_moment_check() (in module causalkit.refutation.orthogonality) oos_moment_check_with_fold_nuisances() (in module causalkit.refutation.orthogonality) orthogonality_derivatives() (in module causalkit.refutation.orthogonality) orthogonality_derivatives_att() (in module causalkit.refutation.orthogonality) outcome (causalkit.data.causaldata.CausalData attribute), [1] (causalkit.data.causaldata.CausalData property), [1] outcome_fit() (causalkit.eda.CausalEDA method) (causalkit.eda.eda.CausalEDA method), [1] outcome_plots() (causalkit.eda.CausalEDA method) (causalkit.eda.eda.CausalEDA method), [1] outcome_stats() (causalkit.eda.CausalEDA method) (causalkit.eda.eda.CausalEDA method), [1] outcome_type (causalkit.data.generators.CausalDatasetGenerator attribute) OutcomeModel (class in causalkit.eda.eda) overlap_diagnostics_att() (in module causalkit.refutation.orthogonality) P plot_ps_overlap() (causalkit.eda.CausalEDA method) (causalkit.eda.eda.CausalEDA method), [1] positivity_check() (causalkit.eda.CausalEDA method) (causalkit.eda.eda.CausalEDA method), [1] (causalkit.eda.eda.PropensityModel method) PropensityModel (class in causalkit.eda.eda) ps_graph() (causalkit.eda.eda.PropensityModel method) R refute_irm_orthogonality() (in module causalkit.refutation) (in module causalkit.refutation.orthogonality) refute_placebo_outcome() (in module causalkit.refutation) (in module causalkit.refutation.placebo) refute_placebo_treatment() (in module causalkit.refutation) (in module causalkit.refutation.placebo) refute_subset() (in module causalkit.refutation) (in module causalkit.refutation.placebo) rng (causalkit.data.generators.CausalDatasetGenerator attribute), [1] roc_auc (causalkit.eda.eda.PropensityModel property) S scores (causalkit.eda.eda.OutcomeModel property) seed (causalkit.data.generators.CausalDatasetGenerator attribute) sensitivity_analysis() (in module causalkit.refutation) (in module causalkit.refutation.sensitivity) sensitivity_analysis_set() (in module causalkit.refutation) (in module causalkit.refutation.sensitivity) shap (causalkit.eda.eda.OutcomeModel property) (causalkit.eda.eda.PropensityModel property) sigma_y (causalkit.data.generators.CausalDatasetGenerator attribute) split_traffic() (in module causalkit.design.traffic_splitter) T target (causalkit.data.causaldata.CausalData property), [1] (causalkit.eda.CausalDataLite attribute) (causalkit.eda.eda.CausalDataLite attribute), [1] target_t_rate (causalkit.data.generators.CausalDatasetGenerator attribute) tau (causalkit.data.generators.CausalDatasetGenerator attribute) theta (causalkit.data.generators.CausalDatasetGenerator attribute) to_causal_data() (causalkit.data.generators.CausalDatasetGenerator method) treatment (causalkit.data.causaldata.CausalData attribute), [1] (causalkit.data.causaldata.CausalData property), [1] (causalkit.eda.CausalDataLite attribute) (causalkit.eda.eda.CausalDataLite attribute), [1] treatment_features() (causalkit.eda.CausalEDA method) (causalkit.eda.eda.CausalEDA method), [1] trim_sensitivity_curve_att() (in module causalkit.refutation.orthogonality) ttest() (in module causalkit.inference) (in module causalkit.inference.att.ttest) U u_strength_t (causalkit.data.generators.CausalDatasetGenerator attribute) u_strength_y (causalkit.data.generators.CausalDatasetGenerator attribute) X x_sampler (causalkit.data.generators.CausalDatasetGenerator attribute)
