Pre-validation Revisited

Pre-validation is a way to build prediction model with two datasets of significantly different feature dimensions. Previous work showed that the asymptotic distribution of test statistic for the pre-validated predictor deviated from a standard Normal, hence will lead to issues in hypothesis tests. In this paper, we revisited the pre-validation procedure and extended the problem formulation without any independence assumption on the two feature sets. We proposed not only an analytical distribution of the test statistics for pre-validated predictor under certain models, but also a generic bootstrap procedure to conduct inference. We showed properties and benefits of pre-validation in prediction, inference and error estimation by simulation and various applications, including analysis of a breast cancer study and a synthetic GWAS example.

Improve ROI with Causal Learning and Conformal Prediction

In the commercial sphere, such as operations and maintenance, advertising, and marketing recommendations, intelligent decision-making utilizing data mining and neural network technologies is crucial, especially in resource allocation to optimize ROI. This study delves into the Cost-aware Binary Treatment Assignment Problem (C-BTAP) across different industries, with a focus on the state-of-the-art Direct ROI Prediction (DRP) method. However, the DRP model confronts issues like covariate shift and insufficient training data, hindering its real-world effectiveness. Addressing these challenges is essential for ensuring dependable and robust predictions in varied operational contexts. This paper presents a robust Direct ROI Prediction (rDRP) method, designed to address challenges in real-world deployment of neural network-based uplift models, particularly under conditions of covariate shift and insufficient training data. The rDRP method, enhancing the standard DRP model, does not alter the model's structure or require retraining. It utilizes conformal prediction and Monte Carlo dropout for interval estimation, adapting to model uncertainty and data distribution shifts. A heuristic calibration method, inspired by a Kaggle competition, combines point and interval estimates. The effectiveness of these approaches is validated through offline tests and online A/B tests in various settings, demonstrating significant improvements in target rewards compared to the state-of-the-art method.