mini_causal.causality.CausalModels¶
- class mini_causal.causality.CausalModels(first_model, second_model)¶
CausalModels class implementation.
The class is mainly deisgned to implement and include Classification and Regression models of all kinds. 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
- average_causal_effect¶
the effect of the feature on the predictions or outcomes.
- Type:
float
- average_causal_effect_resid¶
the average causal effect of the feature on the residuals.
- 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.