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Improve ROI with Causal Learning and Conformal Prediction

Meng Ai, Zhuo Chen, Jibin Wang, Jing Shang, Tao Tao, Zhen Li

TL;DR

The paper addresses ROI-driven resource allocation under binary treatments by analyzing the Direct ROI Prediction (DRP) baseline and its susceptibility to covariate shift and limited data. It introduces robust Direct ROI Prediction (rDRP), which adds Monte Carlo dropout-based uncertainty estimation, conformal prediction for valid ROI intervals, and a heuristic calibration step to improve point estimates without retraining. Across offline simulations on three real-world uplift datasets and online A/B tests, rDRP consistently improves ROI-based ranking under challenging conditions, especially with scarce data or distribution shifts. This work advances interval-aware, scalable ROI prediction for practical budget-allocation decisions in commercial settings.

Abstract

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.

Improve ROI with Causal Learning and Conformal Prediction

TL;DR

The paper addresses ROI-driven resource allocation under binary treatments by analyzing the Direct ROI Prediction (DRP) baseline and its susceptibility to covariate shift and limited data. It introduces robust Direct ROI Prediction (rDRP), which adds Monte Carlo dropout-based uncertainty estimation, conformal prediction for valid ROI intervals, and a heuristic calibration step to improve point estimates without retraining. Across offline simulations on three real-world uplift datasets and online A/B tests, rDRP consistently improves ROI-based ranking under challenging conditions, especially with scarce data or distribution shifts. This work advances interval-aware, scalable ROI prediction for practical budget-allocation decisions in commercial settings.

Abstract

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.
Paper Structure (27 sections, 6 equations, 6 figures, 2 tables, 4 algorithms)

This paper contains 27 sections, 6 equations, 6 figures, 2 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Resource Allocation
  4. Uncertainty Quantification
  5. Robust Optimization
  6. PRELIMINARIES
  7. Notations and Definitions
  8. Problem Formulation
  9. The Goal
  10. METHOD
  11. DRP Model
  12. Limitations of DRP
  13. Covariate Shift
  14. Insufficient Samples
  15. Proposed Method
  16. ...and 12 more sections

Figures (6)

  • Figure 1: Two limitations lead to a decline in DRP's performance on the test set. A larger area under the curve indicates better performance. (a) Covariate shift. (b) Insufficient data.
  • Figure 2: Covariate shift occurs as the distribution of $X_{\rm test}$ changes from $\mathcal{P}$ to $\mathcal{P}_{\rm test}$.
  • Figure 3: $\hat{s}$ may not converge to $s^*$ when training stops due to many factors such as insufficient samples, among other reasons.
  • Figure 4: Architecture of rDRP.
  • Figure 5: Ablation study AUCC results in four settings on dataset CRITEO-UPLIFT v2. (a) SuNo. (b) SuCo. (c) InNo. (d) InCo.
  • ...and 1 more figures

Theorems & Definitions (3)

  • Definition 1: CATE
  • Definition 2: ROI
  • Definition 3: C-BTAP