# Configuration file for the predictive scale sensitivity experiment
# Also compares the nonlinearity of mechanisms
# ==============================================================================

# Defaults
defaults:
  - _self_

  # Simulation settings
  - /simulator: ty_simulator

  # Model parametrization
  - /models@EconML_CausalForestDML: EconML_CausalForestDML
  - /models@EconML_DML: EconML_DML
  - /models@EconML_DMLOrthoForest: EconML_DMLOrthoForest
  - /models@EconML_DRLearner: EconML_DRLearner
  - /models@EconML_DROrthoForest: EconML_DROrthoForest
  - /models@EconML_ForestDRLearner: EconML_ForestDRLearner
  - /models@EconML_LinearDML: EconML_LinearDML
  - /models@EconML_LinearDRLearner: EconML_LinearDRLearner
  - /models@EconML_SparseLinearDML: EconML_SparseLinearDML
  - /models@EconML_SparseLinearDRLearner: EconML_SparseLinearDRLearner #EconML_SparseLinearDRLearner
  - /models@EconML_SLearner_Lasso: EconML_SLearner_Lasso
  - /models@EconML_TLearner_Lasso: EconML_TLearner_Lasso
  - /models@EconML_XLearner_Lasso: EconML_XLearner_Lasso

  - /models@Torch_SLearner: Torch_SLearner
  - /models@Torch_TLearner: Torch_TLearner
  - /models@Torch_XLearner: Torch_XLearner
  - /models@Torch_DRLearner: Torch_DRLearner
  - /models@Torch_RLearner: Torch_RLearner
  - /models@Torch_TARNet: Torch_TARNet
  - /models@Torch_DragonNet: Torch_DragonNet
  - /models@Torch_ULearner: Torch_ULearner
  - /models@Torch_RALearner: Torch_RALearner
  - /models@Torch_PWLearner: Torch_PWLearner
  - /models@Torch_FlexTENet: Torch_FlexTENet
  - /models@Torch_CRFNet_0_01: Torch_CRFNet_0_01
  - /models@Torch_CRFNet_0_001: Torch_CRFNet_0_001
  - /models@Torch_CRFNet_0_0001: Torch_CRFNet_0_0001
  - /models@Torch_ActionNet: Torch_ActionNet


  - /models@DiffPOLearner: DiffPOLearner

# EXPERIMENT AND DATA
# ==============================================================================
experiment_name: "predictive_scale_sensitivity"
# ==============================================================================

# EXPERIMENTAL KNOB
# ==============================================================================
# Cohort sizes to be tested
predictive_scales: [1e-3, 1e-1, 0.5, 2] #[1e-3, 1e-2, 1e-1, 0.5, 1, 2] #, 1.0]
nonlinearity_scales: [0,1] #, 1] # If too unbalanced, the model may give an error because of there being too few instances for a certain class
# ==============================================================================