Bayesian Neural Network Ensemble for Causal Inference Uncertainty

The patent combines multiple neural network models into an ensemble, where each model provides a point estimate. By merging the predictions from multiple models, the system not only maintains accurate point estimate computation but also enables uncertainty quantification through the variance across model predictions, thus resolving the contradiction between obtaining point estimates and quantifying uncertainty.

The patent introduces an intermediary layer between data input and final prediction by training multiple neural network models with different architectures, initializations, or training procedures. This intermediary ensemble structure allows the system to compute point estimates while using the distribution of predictions across models to quantify uncertainty, thereby resolving the information loss about uncertainty.

The patent segments the computational task by dividing it into multiple independent neural network model trainings and predictions. Each model is trained and evaluated separately, allowing for parallel computation. This segmentation enables uncertainty quantification through ensemble variance while managing computational complexity through distributed or parallel processing of individual models.

The patent employs parameter changes by varying initialization seeds, architecture parameters, or training hyperparameters across multiple BNN models. This diversity in model parameters creates different prediction distributions that capture uncertainty, while the systematic variation of parameters allows for efficient generation of multiple models without proportionally increasing overall complexity.

The patent performs preliminary actions by pre-training multiple BNN models on the available data before actual inference. This preliminary training phase establishes the ensemble of models that can then be used for rapid uncertainty quantification during deployment. The computational burden is shifted to the offline training phase, allowing faster processing during actual treatment effect estimation.

The patent creates copies of the neural network model structure multiple times, instantiating several BNN models with different parameter initializations. These model copies are trained independently and then used in parallel for prediction. This copying approach enables uncertainty quantification through ensemble methods while optimizing processing time through parallel execution of identical model architectures.