propensity score matching

Model Perspective

Mar 3, 2024 · Fundamentals

Unraveling Causality: From Frost’s Road Not Taken to Modern Inference

Drawing inspiration from Robert Frost’s poem, this article explains the challenges of causal inference in social sciences, contrasts randomized experiments with observational methods, and introduces key techniques such as propensity score matching, instrumental variables, and regression discontinuity designs for estimating causal effects without randomization.

causal inferenceinstrumental variablesobservational study

0 likes · 12 min read

Unraveling Causality: From Frost’s Road Not Taken to Modern Inference

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

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 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

DaTaobao Tech

Jan 13, 2023 · Artificial Intelligence

Improving Low-Response AB Experiments via Propensity Score Matching and Instrumental Variable Methods

The paper tackles low-response A/B tests by applying instrumental-variable techniques and optimized propensity-score matching, showing that IV methods recover treatment effects for compliant users and that a refined PSM pipeline dramatically boosts lift detection, turning previously non-significant results into statistically significant business insights.

AB testingcausal inferenceinstrumental variables

0 likes · 20 min read

Improving Low-Response AB Experiments via Propensity Score Matching and Instrumental Variable Methods

DataFunTalk

Nov 12, 2022 · Artificial Intelligence

Causal Inference Methods for Large‑Scale Game Analytics: Distributed Propensity Score Matching, Robust Double‑Robust Estimation, and Panel DID

This article introduces causal inference methodologies tailored for game scenarios, discusses the challenges of offline inference on massive data, and presents three distributed solutions—low‑complexity propensity‑score matching, robust double‑robust estimation, and panel difference‑in‑differences—along with their implementation details and performance insights.

causal inferencedistributed computingdouble robust estimation

0 likes · 12 min read

Causal Inference Methods for Large‑Scale Game Analytics: Distributed Propensity Score Matching, Robust Double‑Robust Estimation, and Panel DID

Ctrip Technology

Oct 13, 2022 · Fundamentals

Causal Inference with Propensity Score Matching for Marketing Campaign Value Evaluation

The article explains how causal inference, particularly Propensity Score Matching, can be used to control confounding factors and accurately estimate the incremental value of a marketing campaign when randomized experiments are infeasible, illustrating the method with a real Ctrip project case study.

causal inferencemarketing analyticsobservational study

0 likes · 15 min read

Causal Inference with Propensity Score Matching for Marketing Campaign Value Evaluation

DaTaobao Tech

Apr 18, 2022 · Fundamentals

Propensity Score Matching: Principles, Implementation, and Evaluation

The article explains Propensity Score Matching as a causal inference method, detailing treatment effect concepts, required assumptions, score estimation, various matching algorithms, SQL implementation, quality assessment metrics, and how to estimate ATT using Difference-in-Differences, while outlining workflow steps, trade-offs, and alternatives.

SQLcausal inferencematching algorithms

0 likes · 13 min read

Propensity Score Matching: Principles, Implementation, and Evaluation

DataFunSummit

Mar 27, 2022 · Artificial Intelligence

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

This article explores how combining causal inference with machine learning can uncover subtle correlations in large datasets, detailing user growth metrics, propensity‑score matching, causal recommendation models, heterogeneous treatment effect analysis, and practical strategies for improving retention and activity in recommendation systems.

Recommendation systemscausal inferenceheterogeneous treatment effect

0 likes · 12 min read

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

DataFunTalk

Feb 7, 2022 · Artificial Intelligence

Causal Machine Learning for User Growth: Concepts, Methods, and Applications

This article explores how combining causal inference with machine learning can detect subtle correlations in large datasets, improve user growth metrics such as retention and activity, and presents practical methods like propensity score matching, uplift modeling, HTE analysis, and meta‑learners applied to recommendation systems.

Recommendation systemscausal inferenceheterogeneous treatment effect

0 likes · 13 min read

DataFunSummit

Dec 26, 2021 · Artificial Intelligence

Observational Data Causal Inference and Quasi‑Experimental Methods: Theory, Challenges, and Tencent Case Studies

This article introduces the fundamentals of causal inference with observational data, explains confounding and collider structures, compares observational and experimental approaches, discusses challenges such as Simpson’s paradox, and presents Tencent’s quasi‑experimental applications including DID, regression discontinuity, and uplift modeling.

DIDSimpson's paradoxcausal inference

0 likes · 26 min read

Observational Data Causal Inference and Quasi‑Experimental Methods: Theory, Challenges, and Tencent Case Studies