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Meituan Technology Team
Meituan Technology Team
Jun 19, 2025 · Fundamentals

How to Evaluate Policies Without Experiments: Synthetic Control, Matching, and Causal Impact Explained

Observational research methods—synthetic control, matching, and Causal Impact—offer powerful alternatives to randomized experiments, enabling businesses like Meituan to assess policy effects despite legal and operational constraints, with detailed principles, applications, advantages, limitations, and practical case studies illustrated throughout the guide.

causal inferencematching methodsobservational study
0 likes · 36 min read
How to Evaluate Policies Without Experiments: Synthetic Control, Matching, and Causal Impact Explained
Model Perspective
Model Perspective
May 31, 2025 · Fundamentals

Unlocking Everyday Natural Experiments: Design, Examples, and Analysis

This article explains what natural experiments are, how they differ from controlled trials, and provides practical steps, classic cases, and analytical methods like DID, RDD, and IV to help readers discover and design credible real‑world experiments.

causal inferencedifference-in-differencesinstrumental variables
0 likes · 10 min read
Unlocking Everyday Natural Experiments: Design, Examples, and Analysis
Model Perspective
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.

Propensity Score Matchinginstrumental variablesobservational study
0 likes · 12 min read
Unraveling Causality: From Frost’s Road Not Taken to Modern Inference
Huolala Tech
Huolala Tech
Jan 5, 2024 · Fundamentals

Unlocking Causal Inference: Practical AB Testing and Observational Study Techniques

This article explains how the Huolala data‑science team tackles AB‑testing challenges, pre‑experiment differences, observational (non‑AB) studies, and advanced causal‑inference methods such as CACE, heterogeneous treatment effects, mediation modeling, regression discontinuity, and instrumental variables to derive reliable business insights.

AB testingcausal inferenceheterogeneous treatment effect
0 likes · 11 min read
Unlocking Causal Inference: Practical AB Testing and Observational Study Techniques
Architect
Architect
Oct 14, 2023 · Industry Insights

How to Build a Trustworthy A/B Testing Platform for Complex Multi‑Side Marketplaces

This article explains how Meituan's fulfillment team designs, implements, and operates a reliable A/B testing platform for multi‑side markets, detailing statistical pitfalls, experiment types, traffic-splitting frameworks, and automated analysis pipelines to ensure credible results despite overflow effects, small samples, and fairness constraints.

A/B testingexperiment designmulti‑side marketplace
0 likes · 40 min read
How to Build a Trustworthy A/B Testing Platform for Complex Multi‑Side Marketplaces
DataFunTalk
DataFunTalk
Nov 27, 2022 · Product Management

Challenges of Traditional Experiment Systems and the Vision for Next‑Generation Evaluation Platforms

The article examines why classic A/B testing frameworks struggle with modern internet services—highlighting issues of intervention, measurement, and analysis—while proposing an observational, dynamic, and decision‑oriented next‑generation experiment system that leverages statistical learning and Bayesian optimization.

A/B testingBayesian OptimizationExperiment Platform
0 likes · 11 min read
Challenges of Traditional Experiment Systems and the Vision for Next‑Generation Evaluation Platforms
Ctrip Technology
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.

Propensity Score MatchingValue Estimationcausal inference
0 likes · 15 min read
Causal Inference with Propensity Score Matching for Marketing Campaign Value Evaluation
Liulishuo Tech Team
Liulishuo Tech Team
Oct 26, 2020 · Fundamentals

Causal Inference Methods for Quantifying Product Impact in Data Analytics

This article explains how data analysts can use experimental and observational research methods, including randomized controlled trials, quasi‑experiments, difference‑in‑differences, regression discontinuity, synthetic control, and Bayesian structural time‑series, to assess the causal impact of product and marketing changes on business metrics.

AB testingcausal inferencedifference-in-differences
0 likes · 7 min read
Causal Inference Methods for Quantifying Product Impact in Data Analytics