uplift modeling

DataFunSummit

Jun 22, 2024 · Artificial Intelligence

Applying Causal Inference and Uplift Modeling for User Growth: Concepts, Methods, and Practice

This article introduces causal inference fundamentals, distinguishes correlation from causation, reviews major methodological streams, and demonstrates how uplift and gain models—implemented with T‑learner, S‑learner, and tree‑based approaches—can be applied to user growth and marketing scenarios, including evaluation metrics and future challenges.

A/B testingcausal inferencemachine learning

0 likes · 14 min read

Applying Causal Inference and Uplift Modeling for User Growth: Concepts, Methods, and Practice

DataFunSummit

May 22, 2024 · Artificial Intelligence

Optimizing Coupon Distribution with an Uplift Model

Data analyst Wu Weiwei from eBay China will present how uplift modeling—a causal inference technique—can be applied to e‑commerce coupon distribution, demonstrating methods to identify marketing‑sensitive users, optimize subsidy strategies, and improve business efficiency through data‑driven decision making.

E-commerce Marketingcausal inferencecoupon optimization

0 likes · 3 min read

Optimizing Coupon Distribution with an Uplift Model

DataFunSummit

May 11, 2024 · Artificial Intelligence

Why Causal Inference Matters in Machine Learning and Its Banking Applications

The article explains the necessity of incorporating causal relationships into machine learning, outlines the development of causal science, and details how uplift modeling and causal‑regularized stable learning are applied to marketing and risk control in the banking sector, while also discussing practical challenges and experimental results.

bankingcausal inferencemachine learning

0 likes · 14 min read

Why Causal Inference Matters in Machine Learning and Its Banking Applications

DataFunTalk

Mar 12, 2024 · Artificial Intelligence

Causal Inference with Observational Data for Improving Marketing Efficiency in the Logistics Industry

This article presents a logistics‑focused case study that leverages causal inference techniques, including uplift modeling and entropy‑balancing with flexible spatiotemporal grids, to enhance marketing strategy efficiency using observational data while addressing industry‑specific technical challenges.

Marketing Optimizationcausal inferenceentropy balancing

0 likes · 10 min read

Causal Inference with Observational Data for Improving Marketing Efficiency in the Logistics Industry

DataFunSummit

Dec 24, 2023 · Artificial Intelligence

Causal Inference and Entropy Balancing for Improving Marketing Efficiency in the Logistics Industry

This article presents a logistics‑focused case study that leverages causal inference techniques, especially uplift modeling combined with entropy‑balancing and flexible spatio‑temporal grid partitioning, to enhance marketing strategy efficiency, address confounding bias, and achieve stable, accurate effect estimation across diverse operational scenarios.

AIcausal inferenceentropy balancing

0 likes · 10 min read

Causal Inference and Entropy Balancing for Improving Marketing Efficiency in the Logistics Industry

DataFunSummit

Dec 6, 2023 · Artificial Intelligence

Huya's Experiment Science Platform: Causal Inference, AB Testing, and Uplift Modeling Practices

Huya’s data‑driven experiment platform showcases how causal inference, AB testing, and uplift modeling are applied to advertising, user activation, and growth scenarios, detailing platform evolution, metric design, statistical challenges, and practical solutions such as multi‑test correction, CUPED, RTA, and propensity‑score methods.

AB testingcausal inferencedata science

0 likes · 18 min read

Huya's Experiment Science Platform: Causal Inference, AB Testing, and Uplift Modeling Practices

DataFunTalk

Oct 31, 2023 · Artificial Intelligence

Intelligent Growth Algorithms and Applications in the Smartphone Industry – OPPO Andes Smart Cloud

This article presents OPPO Andes Smart Cloud's intelligent growth algorithm framework for the smartphone sector, detailing industry background, data and model architecture, four real-world application cases—including AIGC content generation, multimodal recommendation, causal inference, and precise advertising—and summarizing key insights from a technical Q&A session.

AIGCRecommendation systemsadvertising optimization

0 likes · 22 min read

Intelligent Growth Algorithms and Applications in the Smartphone Industry – OPPO Andes Smart Cloud

Ctrip Technology

Jul 6, 2023 · Artificial Intelligence

Optimizing SMS Recall Marketing with Response and Uplift Models: A Ctrip Train Ticket Case Study

This article presents a comprehensive case study of Ctrip's train ticket SMS recall business, detailing the design, implementation, and evaluation of response‑based conversion rate models and uplift models to improve marketing ROI through causal inference and machine‑learning techniques.

A/B testingResponse ModelSMS Marketing

0 likes · 14 min read

Optimizing SMS Recall Marketing with Response and Uplift Models: A Ctrip Train Ticket Case Study

DataFunSummit

Jun 27, 2023 · Artificial Intelligence

Intelligent Growth Algorithms and Their Applications in the Smartphone Industry – OPPO Andes Smart Cloud

This article presents OPPO's Andes Smart Cloud team's intelligent growth algorithm architecture, covering industry background, data pipelines, model designs such as uplift, PU‑learning, multimodal AIGC, and their practical applications in content supply, recommendation, precise audience targeting, and ad bidding, followed by a summary and Q&A.

AIGCMobile MarketingRTB

0 likes · 22 min read

Intelligent Growth Algorithms and Their Applications in the Smartphone Industry – OPPO Andes Smart Cloud

Ctrip Technology

Jun 15, 2023 · Fundamentals

Causal Inference Theory and Its Business Applications in Ctrip Train Ticket Operations

This article introduces the fundamental concepts and theoretical frameworks of causal inference, explains Rubin's potential outcomes and Pearl's causal graph models, and demonstrates their practical deployment through uplift modeling, propensity‑score matching, synthetic control, and regression‑discontinuity case studies within Ctrip's train ticket business.

Business Analyticscausal inferencepropensity score matching

0 likes · 15 min read

Causal Inference Theory and Its Business Applications in Ctrip Train Ticket Operations

Didi Tech

Jun 12, 2023 · Artificial Intelligence

Laser: Latent Surrogate Representation Learning for Long-Term Effect Estimation in Ride-Hailing Markets

Laser (Latent Surrogate Representation learning) estimates long‑term ride‑hailing market effects by inferring hidden surrogate variables from short‑term outcomes using an iVAE and inverse‑probability weighting, thereby reducing experiment cost and latency while achieving more accurate causal effect predictions than existing baselines.

IPWRide-hailingcausal inference

0 likes · 9 min read

Laser: Latent Surrogate Representation Learning for Long-Term Effect Estimation in Ride-Hailing Markets

DataFunSummit

Jun 11, 2023 · Artificial Intelligence

Applying Uplift Modeling, PSM Matching, and Spark CausalML for Growth at Tencent Video

This article explains how Tencent Video leverages causal inference techniques—including uplift gain models, propensity‑score‑matching (PSM), and a distributed Spark‑based CausalML library—to identify incremental user effects, evaluate marketing interventions, and improve growth across advertising, internal flow, push notifications, and coupon strategies.

Growth AnalyticsSparkcausal inference

0 likes · 12 min read

Applying Uplift Modeling, PSM Matching, and Spark CausalML for Growth at Tencent Video

DataFunSummit

May 30, 2023 · Artificial Intelligence

Causal Inference in Wing Payment's Intelligent Decision-Making: Exploration and Practice

This article introduces the fundamentals of causal inference, discusses its challenges such as confounding and selection bias, and presents practical applications and methods—including causal discovery, effect estimation, response and uplift models—used in Wing Payment’s intelligent decision‑making scenarios.

A/B testingEffect Estimationcausal inference

0 likes · 12 min read

Causal Inference in Wing Payment's Intelligent Decision-Making: Exploration and Practice

DataFunSummit

May 11, 2023 · Artificial Intelligence

Applying Causal Inference to Financial User Operations: Scenarios, Challenges, and Practices

This article introduces the application of causal inference in financial user operations, outlining typical scenarios such as programmatic advertising and user outreach, discussing data and business challenges, and presenting practical implementations including propensity score matching, sample library construction, experiment design, and full‑stack uplift modeling.

Data Challengescausal inferencefinancial marketing

0 likes · 14 min read

Applying Causal Inference to Financial User Operations: Scenarios, Challenges, and Practices

DataFunTalk

Apr 28, 2023 · Artificial Intelligence

Causal Inference and Uplift Modeling for Insurance Recommendation and Explainability

This article explains how uplift sensitivity prediction, Bayesian causal networks, and decision‑path construction are applied to improve insurance product, coupon, and copy recommendations on the Fliggy platform, detailing modeling approaches, evaluation metrics, and practical outcomes of the causal inference framework.

AB testingbayesian networkscausal inference

0 likes · 16 min read

Causal Inference and Uplift Modeling for Insurance Recommendation and Explainability

DataFunSummit

Apr 6, 2023 · Game Development

Experiment-Driven Advertising and User Operations in Game Growth: Causal Inference, Uplift Modeling, and Practical Pitfalls

This article presents a data‑science‑focused guide on using causal inference and uplift models to drive overseas ad targeting and user‑operation decisions in games, covering audience selection, privacy‑aware exposure correction, bid optimization, experiment design pitfalls, network effects, and practical recommendations.

A/B testingadvertisingcausal inference

0 likes · 18 min read

Experiment-Driven Advertising and User Operations in Game Growth: Causal Inference, Uplift Modeling, and Practical Pitfalls

DataFunTalk

Mar 24, 2023 · Artificial Intelligence

Deep UPLIFT Modeling: Techniques, Challenges, and FinTech Applications

This article provides a comprehensive overview of deep UPLIFT models, covering their fundamentals, key technical challenges such as confounding bias and inductive bias, the evolution of meta‑learner and deep architectures, and practical case studies in financial technology marketing.

Deep LearningFinTechMarketing Optimization

0 likes · 14 min read

Deep UPLIFT Modeling: Techniques, Challenges, and FinTech Applications

DataFunSummit

Feb 10, 2023 · Artificial Intelligence

Multi‑Channel Budget Allocation and Rights Distribution: Modeling, Optimization, and Uplift Strategies

This article presents a comprehensive framework for allocating marketing budgets across multiple channels, designing coupon‑based rights, and distributing them using uplift modeling and optimization techniques to maximize user conversion and overall business ROI.

AI algorithmsbudget allocationcoupon optimization

0 likes · 12 min read

Multi‑Channel Budget Allocation and Rights Distribution: Modeling, Optimization, and Uplift Strategies

DataFunSummit

Feb 5, 2023 · Artificial Intelligence

Key Takeaways from the Causal Inference Summit: Motivation, Applications, Challenges, and Links to A/B Testing, Machine Learning, and Deep Learning

After attending the DataFun causal inference summit, this article outlines why causal analysis matters, its typical use cases, practical challenges, its relationship with A/B testing, and how it integrates with machine learning and deep learning to improve decision‑making and model robustness.

A/B testingDeep Learningcausal inference

0 likes · 10 min read

Key Takeaways from the Causal Inference Summit: Motivation, Applications, Challenges, and Links to A/B Testing, Machine Learning, and Deep Learning

GuanYuan Data Tech Team

Jan 12, 2023 · Artificial Intelligence

How to Boost Beauty Brand Repeat Purchases with AI‑Driven Uplift Modeling

This article explains how beauty brands can increase repeat purchase rates by building high‑potential member prediction models, applying tiered segmentation, and leveraging various AI‑powered models—including natural repurchase, purchase power, marketing response, and uplift models—to optimize targeting, ROI, and overall sales performance.

AIcustomer retentionmarketing analytics

0 likes · 20 min read