A Qini-based uplift model improves retention marketing campaign performance.
problem Isolating the marketing effect of a campaign and identifying responsive customers.
method Qini-based uplift regression model using logistic regression.
result Qini-optimized uplift models improve performance and provide interpretable models.
Proposes a resampling method to compare uplift models with uncertainty.
problem Uncertainty in estimating uplift curves when full population data is unavailable.
method Two-step sampling procedure and resampling-based approach.
result Validates the proposed method through simulations and real data applications.
The paper introduces revenue uplift modeling to maximize marketing profits.
problem Maximizing incremental sales versus maximizing incremental revenue in marketing campaigns.
method Response transformation and two-stage models to decompose campaign profit.
result Revenue uplift modeling can improve campaign profit substantially.
A new method for uplift modeling using learning-to-rank techniques.
problem Improving customer targeting in marketing and retention.
method Unified formalization of uplift measures, learning-to-rank with PCG metric, LambdaMART optimization.
result Improved results compared to standard learning-to-rank metrics and state-of-the-art uplift modeling.
Graph neural networks integrate causal knowledge for more accurate uplift modeling.
problem Identifying the most effective treatments and clients for marketing interventions.
method Combining graph neural networks with causal knowledge to estimate uplift values.
result The proposed method outperforms traditional approaches in predicting uplift values with minimal errors.
Uplift modeling is an area of machine learning which aims at predicting the causal effect of some action on a given individual. The action may be a medical procedure, marketing campaign, or any other circumstance controlled by the experimenter. Building an uplift model requires two training sets: the treatment group, w…
Our paper improves uplift model evaluation on randomized controlled trials (RCT) data.
problem Variance in uplift evaluation metrics makes their signals arbitrary and unreliable.
method Theoretical analysis and statistical adjustment of the outcome to reduce variance.
result Variance reduction methods improve uplift evaluation metrics on RCT data.
New neural model improves uplift modeling accuracy.
problem Improving accuracy in uplift modeling for specific treatments.
method Developed a twin neural network architecture with a new loss function.
result Proposed model outperforms existing methods in simulations and real data.
New neural network method improves uplift modeling accuracy.
problem Overfitting in uplift modeling.
method Jointly optimizing difference in conditional means and transformed outcome losses using neural networks.
result Improves state-of-the-art uplift modeling on synthetic and real data.
New feature selection methods improve uplift modeling accuracy.
problem Overfitting and poor interpretability in feature selection for uplift models.
method Explicitly designed feature selection methods inspired by statistics and information theory.
result Proposed methods outperform traditional feature selection methods in uplift modeling.
This paper enhances uplift modeling for multi-treatment marketing campaigns.
problem Optimizing marketing strategies by selecting individuals likely to respond to different treatments.
method Leveraging score ranking and calibration techniques.
result Improves overall performance of marketing campaigns.
A framework for causal classification using uplift and causal heterogeneity methods.
problem Predicting the effect of interventions on different individuals from data.
method Causal classification framework using off-the-shelf supervised methods.
result Framework works for causal classification and uplift modelling, competitive with other methods.
Heteroskedasticity biases uplift model rankings, leading to inefficient treatment allocation.
problem Bias in uplift model rankings due to heteroskedasticity.
method Theoretical analysis and simulation on real-world data.
result Heteroskedasticity can cause individuals with high treatment effects to be ranked at the bottom, leading to inefficient treatment allocation.
Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of interventions such as marketing campaigns and product designs. Uplift modeling can be used to estimate which users are likely to benefit from …
UCB algorithms estimate uplifts in multi-variable reward systems.
problem Marketing campaigns and recommender systems with multiple outcome variables.
method UCB-style algorithms for estimating uplifts over a baseline.
result Sublinear regret bounds for multiple problem variants.
Randomized experiments have been critical tools of decision making for decades. However, subjects can show significant heterogeneity in response to treatments in many important applications. Therefore it is not enough to simply know which treatment is optimal for the entire population. What we need is a model that corr…
Uplift modeling is aimed at estimating the incremental impact of an action on an individual's behavior, which is useful in various application domains such as targeted marketing (advertisement campaigns) and personalized medicine (medical treatments). Conventional methods of uplift modeling require every instance to be…
Customer scoring models are the core of scalable direct marketing. Uplift models provide an estimate of the incremental benefit from a treatment that is used for operational decision-making. Training and monitoring of uplift models require experimental data. However, the collection of data under randomized treatment as…
FairUDT uses uplift decision trees to detect and mitigate discrimination in training data.
problem Bias in machine learning classifiers due to historical discrimination or underrepresentation of minority groups.
method Integrates uplift modeling with decision trees and introduces a modified leaf relabeling approach for fairness.
result Achieves an acceptable accuracy-discrimination tradeoff while maintaining interpretability.
A new uplift modeling approach uses binary treatment indicators more efficiently.
problem Lack of full value utilization in binary uplift modeling.
method Design a novel transformed outcome for binary target variables.
result Our new approach outperforms traditional methods in synthetic and real-world datasets.
A new method flips class values to address class and treatment imbalance in uplift modeling and HTE.
problem Class and treatment imbalance in imbalanced RCT data.
method Class flipping approach to address imbalance without distorting predictions.
result The method does not distort predicted effects and does not require calibration.
Uplift modeling aims to directly model the incremental impact of a treatment on an individual response. In this work, we address the problem from a new angle and reformulate it as a Markov Decision Process (MDP). We conducted extensive experiments on both a synthetic dataset and real-world scenarios, and showed that ou…
Proposes a generalized causal tree for handling multiple treatments in uplift modeling.
problem Handling multiple treatments in uplift modeling.
method Generalizes causal tree algorithm to handle multiple discrete and continuous-valued treatments.
result Demonstrates improved performance over existing methods in experiments and real data examples.
Unified survey of treatment effect heterogeneity and uplift modeling methods.
problem Estimating heterogeneous treatment effects and uplift modeling.
method Unified survey of treatment effect heterogeneity and uplift modeling approaches.
result Unified notations for comparing methods and applications in personalized marketing, medicine, and social studies.
Bridges uplift modeling and sequential decision-making with online budget allocation.
problem Treatment allocation under budget constraints in digital advertising.
method Budget-Constrained Causal Bandits (BCCB) integrates learning, exploration, and budget pacing.
result Data-efficiency crossover: BCCB operates effectively from the first user, 3-5x lower performance variance.
Large dataset released for ITE and UM research.
problem Estimating causal impact of actions in various sectors.
method Release of a large dataset, formalization of UM, synthetic response surfaces, heterogeneous treatment assignment.
result Validation of ITE prediction and UM methods with high statistical significance.
The paper compares methods for estimating individual treatment effects.
problem Estimating the optimal treatment effect for each individual.
method Comparison of machine learning methods for individual treatment effect estimation.
result Combination of Logistic Regression and Difference Score method, as well as Uplift Random Forest method, provides the best prediction accuracy.
Dynamic promotion optimization for e-commerce platforms within financial constraints.
problem Balancing promotional costs with incremental revenue for sustainable growth.
method Knapsack Problem formulation for dynamic optimization, Retrospective Estimation, online-dynamic calibration.
result Significant increase in target outcome while staying within financial constraints.
GCF estimates heterogeneous treatment effects for continuous treatments in online marketplaces.
problem Estimating heterogeneous treatment effects for continuous treatments in online marketplaces.
method Kernel-based doubly robust estimator and distance-based splitting criterion.
result GCF estimates heterogeneous treatment effects for continuous treatments effectively.
This work explores the idea of a causal contextual multi-armed bandit approach to automated marketing, where we estimate and optimize the causal (incremental) effects. Focusing on causal effect leads to better return on investment (ROI) by targeting only the persuadable customers who wouldn't have taken the action orga…
Ethereum's Pectra upgrade introduces 0x02 compounding validators, offering higher stake and potential APR uplifts.
problem Staking rewards on Ethereum
method Analyzing compounding effects on consensus-layer rewards
result 0x02 validators achieve modestly higher median CL APR
Applying causal inference models in areas such as economics, healthcare and marketing receives great interest from the machine learning community. In particular, estimating the individual-treatment-effect (ITE) in settings such as precision medicine and targeted advertising has peaked in application. Optimising this IT…
We study stratified G-structures in N=2 compactifications of M-theory on eight-manifolds M using the uplift to the auxiliary nine-manifold M^=M×S1. We show that the cosmooth generalized distribution D^ on M^ which arises in this formalism may have pointwise transverse or…
The paper simplifies complex jump-diffusion markets to complete models.
problem Pricing and hedging derivatives in incomplete jump-diffusion markets.
method Filtration reduction to a complete market, then consistency to original market.
result A unique equivalent martingale measure is obtained for pricing.
Proposes a new method to optimize treatment allocation with budget constraints.
problem Optimizing treatment allocation with budget constraints in marketing.
method Learning to rank for profit maximization.
result Learning to rank can maximize the area under a policy's incremental profit curve.
The paper uses machine learning to find causal rules from business process logs.
problem Discovering causal relationships in business process logs.
method Action rule mining followed by causal machine learning (uplift trees).
result Identifies treatments with high causal effect on outcomes.
A new model predicts fashion demand 6-12 months ahead, boosting retailer profits.
problem Accurate demand forecasting for fashion retailers with short product life cycles.
method Product age-based forecast model, incorporating unique feature engineering.
result Significant revenue uplift of 41% compared to retailer's plan.
Optimizes treatment allocation in networks considering indirect effects.
problem Finding optimal treatment allocation in network settings with interference.
method OTAPI: Optimizing Treatment Allocation in the Presence of Interference, integrating causal estimators into IM algorithms.
result OTAPI outperforms classic IM and UM approaches on synthetic and semi-synthetic datasets.
We evaluate the applicability of the generic Vickrey-Clarke-Groves (VCG) mechanism as an antimonopoly measure against a profit-maximizing producer with market power operating a portfolio of generating units at the centralized two-settlement energy market. The producer may indicate in its bid not only the altered cost f…
In recent years, the softmax model and its fast approximations have become the de-facto loss functions for deep neural networks when dealing with multi-class prediction. This loss has been extended to language modeling and recommendation, two fields that fall into the framework of learning from Positive and Unlabeled d…
With growing consumer adoption of online grocery shopping through platforms such as Amazon Fresh, Instacart, and Walmart Grocery, there is a pressing business need to provide relevant recommendations throughout the customer journey. In this paper, we introduce a production within-basket grocery recommendation system, R…
A new ride-hailing subsidy system uses deep causal networks to estimate consumer elasticity.
problem Estimating consumer elasticity with subsidies in ride-hailing industry.
method Introduces a consumer subsidizing system using deep causal networks to address confounding effects.
result Effective in estimating the uplift effect of subsidies without confounding.
Enhances GNNs to better capture local graph structures.
problem Limited expressiveness of GNNs due to star-shaped message passing.
method Extends local aggregation to subgraph patterns using GNN encoders.
result Significantly improved performance on graph ML tasks.
Many continuous control tasks have easily formulated objectives, yet using them directly as a reward in reinforcement learning (RL) leads to suboptimal policies. Therefore, many classical control tasks guide RL training using complex rewards, which require tedious hand-tuning. We automate the reward search with AutoRL,…
Study shows sample noise impacts active learning performance.
problem Impact of sample noise on active learning performance.
method Proposed Incremental Weighted K-Means for noisy samples.
result Robust sampler improves synthetic tasks but only marginally in real-life.
Before A/B testing online a new version of a recommender system, it is usual to perform some offline evaluations on historical data. We focus on evaluation methods that compute an estimator of the potential uplift in revenue that could generate this new technology. It helps to iterate faster and to avoid losing money b…
Optimizes non-linear outcomes from summed contributions.
problem Maximizing a non-linear function of summed small contributions.
method Derives a scalable descent algorithm leveraging concentration properties.
result Directly optimizes for stated objective, e.g., A/B test success criterion.
New method calibrates heterogeneous treatment effect models.
problem Difficulty in estimating and calibrating heterogeneous treatment effects.
method Defined and proposed a robust estimator for HTE calibration, based on doubly robust treatment effect estimators.
result Proposed method evaluates calibration of learned HTE models, addressing overfitting and high-dimensionality.