odtlearn.utils.validation

Module Contents

Functions

check_ipw(X, ipw)	This function checks the propensity weights and counterfactual predictions
check_y_hat(X, treatments, y_hat)	This function checks the propensity weights and counterfactual predictions
check_y(X, y)	This function checks the shape and contents of the observed outcomes
check_columns_match(original_columns, new_data)	Check that the column names of a new data frame match the column names used when used to fit the model
check_binary(df)
check_integer(df)
check_same_as_X(X, X_col_labels, G, G_label)	Check if a DataFrame G has the columns of X

odtlearn.utils.validation.check_ipw(X, ipw)

This function checks the propensity weights and counterfactual predictions

Parameters:

X: The input/training data

ipw: A vector or array-like object for inverse propensity weights. Only needed when running IPW/DR

Returns:

The converted version of ipw after passing the series of checks

odtlearn.utils.validation.check_y_hat(X, treatments, y_hat)

This function checks the propensity weights and counterfactual predictions

Parameters:

X: The input/training data

treatments: A vector of the unique treatment values in the dataset.

y_hat: A multi-dimensional array-like object for counterfactual predictions. Only needed when running DM/DR

Returns:

The converted versions of ipw and y_hat after passing the series of checks

odtlearn.utils.validation.check_y(X, y)

This function checks the shape and contents of the observed outcomes

Parameters:

X: The input/training data

y: A vector or array-like object for the observed outcomes corresponding to treatment t

Returns:

The converted version of y after passing the series of checks

odtlearn.utils.validation.check_columns_match(original_columns, new_data)

Check that the column names of a new data frame match the column names used when used to fit the model

Parameters:

original_columns: List of column names from the data set used to fit the model

new_data: The numpy matrix or pd dataframe new data set for

which we want to make predictions

Returns:

ValueError if column names do not match, otherwise None

odtlearn.utils.validation.check_binary(df)

odtlearn.utils.validation.check_integer(df)

odtlearn.utils.validation.check_same_as_X(X, X_col_labels, G, G_label)

Check if a DataFrame G has the columns of X