observational data

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

Jan 19, 2024 · Fundamentals

Causal Inference and Its Applications in Medical Research

This article reviews the importance of causal inference in medicine, covering historical perspectives on disease causation, epidemiological methods such as Mill's rules and cohort studies, modern techniques like Mendelian randomization, and future research directions in causal graph learning and AI integration.

Mendelian randomizationcausal inferenceepidemiology

0 likes · 13 min read

Causal Inference and Its Applications in Medical Research

DataFunSummit

Sep 1, 2023 · Artificial Intelligence

Observational Causal Inference and De‑Confounding Techniques for Industrial Applications

This article introduces the fundamentals of causal inference from observational data, explains confounding and the SUTVA assumptions, presents the do‑operator, and details four de‑confounding strategies—including RCT‑based resampling, feature‑decomposition, double machine learning, and back‑/front‑door adjustments—followed by real‑world applications in recommendation systems and resource allocation.

Machine LearningRecommendation systemscausal inference

0 likes · 22 min read

Observational Causal Inference and De‑Confounding Techniques for Industrial Applications

Model Perspective

Sep 13, 2022 · Fundamentals

Why Linear Regression Is Surprisingly Powerful for Causal Inference

This article explains how linear regression can be used to estimate average causal effects, handle bias, and draw valid conclusions from both randomized experiments and observational data, while illustrating the theory with concrete examples and visualizations.

average treatment effectcausal inferencelinear regression

0 likes · 16 min read

Why Linear Regression Is Surprisingly Powerful for Causal Inference

DaTaobao Tech

Mar 15, 2022 · Fundamentals

Introduction to Causal Inference and Instrumental Variables

The article introduces causal inference for observational business data, contrasts methods that require observed confounders with instrumental-variable techniques that can address unobserved confounding, explains the three core IV assumptions plus homogeneity or monotonicity, illustrates the Wald estimator, warns about weak instruments, and urges careful application.

causal inferenceinstrumental variablesmethodology

0 likes · 24 min read

Introduction to Causal Inference and Instrumental Variables

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

DataFunTalk

Dec 6, 2021 · Artificial Intelligence

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

This article provides a comprehensive overview of causal inference on observational data, explaining confounding and collider structures, experimental solutions, the differences between observational and experimental data, challenges such as Simpson's paradox, and detailed Tencent case studies using DID, regression discontinuity, and uplift modeling to guide practical analysis.

DIDMachine Learningcausal inference

0 likes · 26 min read

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