Interaction Term

The Interaction Term Approach (The True Lift Model, The Dummy Variable Approach).

Based on

Kuchumov, A. pyuplift: Lightweight uplift modeling framework for Python. (2019). URL: https://github.com/duketemon/pyuplift. License: https://github.com/duketemon/pyuplift/blob/master/LICENSE.

Lo, VSY. (2002). “The true lift model: a novel data mining approach to response modeling in database marketing”. In:SIGKDD Explor4 (2), 78–86. URL: https://dl.acm.org/citation.cfm?id=772872

Devriendt, F. et al. (2018). A Literature Survey and Experimental Evaluation of the State-of-the-Art in Uplift Modeling: A Stepping Stone Toward the Development of Prescriptive Analytics. Big Data, Vol. 6, No. 1, March 1, 2018, pp. 1-29. Codes found at: data-lab.be/downloads.php.

Contents

InteractionTerm Class

fit, predict, predict_proba

class causeinfer.standard_algorithms.interaction_term.InteractionTerm(model=None)

fit(X, y, w)

Trains a model given covariates, responses and assignments.

Parameters:

Xnumpy.ndarray(num_units, num_features)int, float

Matrix of covariates.

ynumpy.ndarray(num_units,)int, float

Vector of unit responses.

wnumpy.ndarray(num_units,)int, float

Vector of original treatment allocations across units.

Returns:

selfcauseinfer.standard_algorithms.InteractionTerm

A trained model.

predict(X)

Predicts a causal effect given covariates.

Parameters:

Xnumpy.ndarray(num_units, num_features)int, float

New data on which to make predictions.

Returns:

predictionsnumpy.ndarray(num_units, 2)float

Predicted causal effects for all units given a 1 and 0 interaction term.

predict_proba(X)

Predicts the probability that a subject will be a given class given covariates.

Parameters:

Xnumpy.ndarray(num_units, num_features)int, float

New data on which to make predictions.

Returns:

probasnumpy.ndarray(num_units, 2)float

Predicted causal probabilities for all units given a 1 and 0 interaction term.