mini_causal.causality.CausalModelsRegression¶
- class mini_causal.causality.CausalModelsRegression(first_model, second_model)¶
CausalModelsRegression class implementation.
The class is mainly deisgned to cover Regression models for tabular data. The aim is to measure the imapact that one model has on the predictions compared to the second one .
In other words,A/B Testing using causality to measure impact
- Parameters:
first_model – the first trained model
second_model – the second trained model
- individual_causal_effects¶
an array of the individual causal effects for the predictions
- Type:
np.ndarray
- individual_causal_effects_resid¶
an array of the individual causal effects for the residuals
- Type:
np.ndarray
- individual_causal_effects_probs¶
an array of the individual causal effects for the probabilities
- Type:
np.ndarray
- average_causal_effect¶
the average causal effect of the first model
- Type:
float
- average_causal_effect_resid¶
the average causal effect of the first model on the residuals
- Type:
float
- average_causal_effect_probs¶
the average causal effect of the first model on the predicted probabilities
- Type:
float
- __init__(first_model, second_model)¶
Methods
__init__(first_model, second_model)The average difference in the predictions for the two models.
The average difference in the effects of the residuals.
The individual causal effects of the predicted outcomes.
The individual causal effects of the residuals.