official/recommendation/uplift/README.md

Uplift Modeling Library

Uplift modeling is a predictive modeling technique that models the incremental impact of a treatment on an individual. This repository contains a suite of tools to build, train, evaluate and export an uplift model in TensorFlow 2. All components are built in a modular fashion and integrate well with other TF-based systems such as TFX, Tensorflow Transform (TFT) and Tensorflow Model Analysis (TFMA). We use Keras as our modeling framework.

The library is divided into the following key directories:

• layers: Keras layers related to uplift modeling.
• models: Keras models that contain all the necessary logic to train, evaluate and do inference on an uplift model.
• metrics: Keras metrics used in uplift modeling.
• losses: Keras losses used in uplift modeling.

Two Tower Uplift Model

The initial release focuses on the family of models that follow a two tower uplift network architecture. The architecture draws inspiration from several related works in the field of treatment effect estimation [^1] [^2] [^3] [^4] [^5].

• Inputs: a mapping from feature names to feature tensors. The tensors can be of different types, eg tf.Tensor, tf.SparseTensor and tf.RaggedTensor.
• Backbone: a trainable network that computes an embedding from the inputs shared between the control and treatment arms.
• Control & Treatment Feature Encoders: trainable networks that compute embeddings from control and treatment speficic features.
• Control & Treatment Feature Combiners: methods to combine the backbone's shared embedding with the control/treatment specific embeddings.
• Control Tower: trainable network with zero or more hidden layers that learns from control examples only.
• Treatment Tower: trainable network with zero or more hidden layers that learns from treatment examples only.
• Logits Head: computes control and treatment logits. At training time, the gradient flows from the control logits for control examples and from the treatment logits for the treatment examples.

Example usage:

# Create a two tower uplift model.
uplift_network = two_tower_uplift_network.TwoTowerUpliftNetwork(
    backbone=encoders.concat_features.ConcatFeatures(
        feature_names=["shared_feature_1", "shared_feature_2"]
    ),
    treatment_feature_encoder=encoders.concat_features.ConcatFeatures(
        feature_names=["treatment_feature_1", "treatment_feature_2"]
    ),
    treatment_input_combiner=tf.keras.layers.Concatenate(),
    treatment_tower=tf.keras.Sequential([
        tf.keras.layers.Dense(64, activation="relu"),
        tf.keras.layers.Dropout(0.1),
    ]),
    control_tower=tf.keras.Sequential([
        tf.keras.layers.Dense(128, activation="relu"),
        tf.keras.layers.Dropout(0.1),
        tf.keras.layers.Dense(32, activation="relu")
    ]),
    logits_head=two_tower_logits_head.TwoTowerLogitsHead(
        control_head=tf.keras.layers.Dense(1),
        treatment_head=tf.keras.layers.Dense(1),
    ),
)
model = two_tower_uplift_model.TwoTowerUpliftModel(
  treatment_indicator_feature_name="is_treatment",
  uplift_network=uplift_network,
)

# Compile and train the model.
model.compile(
  optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.05),
  loss=true_logits_loss.TrueLogitsLoss(tf.keras.losses.mean_squared_error),
  metrics=[
    treatment_fraction.TreatmentFraction(),
    uplift_mean.UpliftMean(),
    label_mean.LabelMean(),
    label_variance.LabelVariance(),
  ]
)
model.fit(dataset, epochs=10)

[^1]: Johansson, Fredrik, Uri Shalit, and David Sontag. "Learning representations for counterfactual inference." International conference on machine learning. PMLR, 2016. [^2]: Shalit, Uri, Fredrik D. Johansson, and David Sontag. "Estimating individual treatment effect: generalization bounds and algorithms." International conference on machine learning. PMLR, 2017. [^3]: Johansson, Fredrik D., et al. "Learning weighted representations for generalization across designs." arXiv preprint arXiv:1802.08598 (2018). [^4]: Hassanpour, Negar and Russell Greiner. “CounterFactual Regression with Importance Sampling Weights.” International Joint Conference on Artificial Intelligence (2019). [^5]: Hassanpour, Negar and Russell Greiner. “Learning Disentangled Representations for CounterFactual Regression.” International Conference on Learning Representations (2020).