Interpretable Multiple Treatment Revenue Uplift Modeling

• URL: http://arxiv.org/abs/2101.03336v1
• Date: Sat, 9 Jan 2021 11:29:00 GMT
• Title: Interpretable Multiple Treatment Revenue Uplift Modeling
• Authors: Robin M. Gubela and Stefan Lessmann
• Abstract summary: 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.
• Score: 4.9571232160914365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Big data and business analytics are critical drivers of business and societal transformations. Uplift models support a firm's decision-making by predicting the change of a customer's behavior due to a treatment. Prior work examines models for single treatments and binary customer responses. The paper extends corresponding approaches by developing uplift models for multiple treatments and continuous outcomes. This facilitates selecting an optimal treatment from a set of alternatives and estimating treatment effects in the form of business outcomes of continuous scale. Another contribution emerges from an evaluation of an uplift model's interpretability, whereas prior studies focus almost exclusively on predictive performance. To achieve these goals, the paper develops revenue uplift models for multiple treatments based on a recently introduced algorithm for causal machine learning, the causal forest. Empirical experimentation using two real-world marketing data sets demonstrates the advantages of the proposed modeling approach over benchmarks and standard marketing practices.

Related papers

• MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation [80.47072100963017]
  We introduce a novel and low-compute algorithm, Model Merging with Amortized Pareto Front (MAP) MAP efficiently identifies a set of scaling coefficients for merging multiple models, reflecting the trade-offs involved. We also introduce Bayesian MAP for scenarios with a relatively low number of tasks and Nested MAP for situations with a high number of tasks, further reducing the computational cost of evaluation.
  arXiv  Detail & Related papers  (2024-06-11T17:55:25Z)
• Decision-Focused Forecasting: Decision Losses for Multistage Optimisation [0.0]
  We propose decision-focused forecasting, a multiple-implicitlayer model which in its training accounts for the intertemporal decision effects of forecasts using differentiable optimisation. We present an analysis of the gradients produced by this model showing the adjustments made to account for the state-path caused by forecasting. We demonstrate an application of the model to an energy storage arbitrage task and report that our model outperforms existing approaches.
  arXiv  Detail & Related papers  (2024-05-23T15:48:46Z)
• Predictive Churn with the Set of Good Models [64.05949860750235]
  We study the effect of conflicting predictions over the set of near-optimal machine learning models. We present theoretical results on the expected churn between models within the Rashomon set. We show how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications.
  arXiv  Detail & Related papers  (2024-02-12T16:15:25Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Uplift vs. predictive modeling: a theoretical analysis [1.2412255325209152]
  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.
  arXiv  Detail & Related papers  (2023-09-21T12:59:17Z)
• Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation [12.37745209793872]
  We introduce a model-agnostic framework for gathering data to evaluate and improve contextual decision making. Our method is used for the data-efficient evaluation of the regret of past treatment assignments.
  arXiv  Detail & Related papers  (2022-07-12T01:20:11Z)
• Extending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes [62.997667081978825]
  The paper presents an approach to process mining providing semi-automatic support to model optimization. A model simplification approach is proposed, which essentially abstracts the raw model at the desired granularity. We aim to demonstrate the capabilities of the technological solution using three datasets from different applications in the healthcare domain.
  arXiv  Detail & Related papers  (2022-06-10T16:20:59Z)
• Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation [3.728946517493471]
  MEEE is a model-ensemble method that consists of optimistic exploration and weighted exploitation. Our approach outperforms other model-free and model-based state-of-the-art methods, especially in sample complexity.
  arXiv  Detail & Related papers  (2021-07-05T07:18:20Z)
• A Twin Neural Model for Uplift [59.38563723706796]
  Uplift is a particular case of conditional treatment effect modeling. We propose a new loss function defined by leveraging a connection with the Bayesian interpretation of the relative risk. We show our proposed method is competitive with the state-of-the-art in simulation setting and on real data from large scale randomized experiments.
  arXiv  Detail & Related papers  (2021-05-11T16:02:39Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• 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)

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.