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observational study

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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.

Difference-in-Differencescausal inferenceinstrumental 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.

causal inferenceinstrumental variablesobservational study
0 likes · 12 min read
Unraveling Causality: From Frost’s Road Not Taken to Modern Inference
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.

causal inferencemarketing analyticsobservational study
0 likes · 15 min read
Causal Inference with Propensity Score Matching for Marketing Campaign Value Evaluation
Model Perspective
Model Perspective
Sep 16, 2022 · Fundamentals

Why Adding Non‑Confounding Controls Can Boost Causal Estimates (And When They Hurt)

This article explains how adding covariates that are not confounders can reduce outcome variance and improve causal inference, while controlling for variables that only predict treatment may introduce selection bias and inflate estimation error.

Variance Reductioncausal inferencecontrol variables
0 likes · 21 min read
Why Adding Non‑Confounding Controls Can Boost Causal Estimates (And When They Hurt)
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 testingDifference-in-DifferencesExperimental design
0 likes · 7 min read
Causal Inference Methods for Quantifying Product Impact in Data Analytics