Uplift vs. predictive modeling: a theoretical analysis

• URL: http://arxiv.org/abs/2309.12036v1
• Date: Thu, 21 Sep 2023 12:59:17 GMT
• Title: Uplift vs. predictive modeling: a theoretical analysis
• Authors: Th\'eo Verhelst, Robin Petit, Wouter Verbeke, Gianluca Bontempi
• Abstract summary: This paper presents a comprehensive treatment of the subject, starting from firm theoretical foundations and highlighting the parameters that influence the performance of the uplift and predictive approaches. The focus of the paper is on a binary outcome case and a binary action, and the paper presents a theoretical analysis of uplift modeling, comparing it with the classical predictive approach.
• Score: 1.2412255325209152
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Despite the growing popularity of machine-learning techniques in decision-making, the added value of causal-oriented strategies with respect to pure machine-learning approaches has rarely been quantified in the literature. These strategies are crucial for practitioners in various domains, such as marketing, telecommunications, health care and finance. This paper presents a comprehensive treatment of the subject, starting from firm theoretical foundations and highlighting the parameters that influence the performance of the uplift and predictive approaches. The focus of the paper is on a binary outcome case and a binary action, and the paper presents a theoretical analysis of uplift modeling, comparing it with the classical predictive approach. The main research contributions of the paper include a new formulation of the measure of profit, a formal proof of the convergence of the uplift curve to the measure of profit ,and an illustration, through simulations, of the conditions under which predictive approaches still outperform uplift modeling. We show that the mutual information between the features and the outcome plays a significant role, along with the variance of the estimators, the distribution of the potential outcomes and the underlying costs and benefits of the treatment and the outcome.

Related papers

• Achieving Fairness in Predictive Process Analytics via Adversarial Learning [50.31323204077591]
  This paper addresses the challenge of integrating a debiasing phase into predictive business process analytics. Our framework leverages on adversial debiasing is evaluated on four case studies, showing a significant reduction in the contribution of biased variables to the predicted value.
  arXiv  Detail & Related papers  (2024-10-03T15:56:03Z)
• Counterfactual Fairness by Combining Factual and Counterfactual Predictions [18.950415688199993]
  In high-stake domains such as healthcare and hiring, the role of machine learning (ML) in decision-making raises significant fairness concerns. This work focuses on Counterfactual Fairness (CF), which posits that an ML model's outcome on any individual should remain unchanged if they had belonged to a different demographic group. We provide a theoretical study on the inherent trade-off between CF and predictive performance in a model-agnostic manner.
  arXiv  Detail & Related papers  (2024-09-03T15:21:10Z)
• Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach [6.112119533910774]
  This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine-tuned with a novel combination of instruction-based techniques and quantized low-rank adaptation (QLoRA) compression. Our methodology integrates 'base factors', such as financial metric growth and earnings transcripts, with 'external factors', including recent market indices performances and analyst grades, to create a rich, supervised dataset. This study not only demonstrates the power of integrating cutting-edge AI with fine-tuned financial data but also paves the way for future research in enhancing AI-driven financial analysis tools.
  arXiv  Detail & Related papers  (2024-08-13T04:53:31Z)
• See Further for Parameter Efficient Fine-tuning by Standing on the Shoulders of Decomposition [56.87609859444084]
  parameter-efficient fine-tuning (PEFT) focuses on optimizing a select subset of parameters while keeping the rest fixed, significantly lowering computational and storage overheads. We take the first step to unify all approaches by dissecting them from a decomposition perspective. We introduce two novel PEFT methods alongside a simple yet effective framework designed to enhance the performance of PEFT techniques across various applications.
  arXiv  Detail & Related papers  (2024-07-07T15:44:42Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Interpretable Multiple Treatment Revenue Uplift Modeling [4.9571232160914365]
  Uplift models support a firm's decision-making by predicting the change of a customer's behavior due to a treatment. The paper extends corresponding approaches by developing uplift models for multiple treatments and continuous outcomes.
  arXiv  Detail & Related papers  (2021-01-09T11:29:00Z)
• Counterfactual Representation Learning with Balancing Weights [74.67296491574318]
  Key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. We develop an algorithm for flexible, scalable and accurate estimation of causal effects.
  arXiv  Detail & Related papers  (2020-10-23T19:06:03Z)
• Double Robust Representation Learning for Counterfactual Prediction [68.78210173955001]
  We propose a novel scalable method to learn double-robust representations for counterfactual predictions. We make robust and efficient counterfactual predictions for both individual and average treatment effects. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
  arXiv  Detail & Related papers  (2020-10-15T16:39:26Z)
• Introduction to Rare-Event Predictive Modeling for Inferential Statisticians -- A Hands-On Application in the Prediction of Breakthrough Patents [0.0]
  We introduce a machine learning (ML) approach to quantitative analysis geared towards optimizing the predictive performance. We discuss the potential synergies between the two fields against the backdrop of this, at first glance, target-incompatibility. We are providing a hands-on predictive modeling introduction for a quantitative social science audience while aiming at demystifying computer science jargon.
  arXiv  Detail & Related papers  (2020-03-30T13:06:25Z)
• A Survey on Causal Inference [64.45536158710014]
  Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics. Various causal effect estimation methods for observational data have sprung up.
  arXiv  Detail & Related papers  (2020-02-05T21:35:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.