BestHub
Sign in
Tagged articles
9 articles
Page 1 of 1
Didi Tech
Didi Tech
Jul 24, 2025 · Artificial Intelligence

How to Estimate Long‑Term Heterogeneous Dose‑Response Curves with Unobserved Confounding

This article presents a data‑fusion framework that combines long‑term observational data and short‑term randomized experiments to identify and estimate long‑term heterogeneous dose‑response curves under continuous treatments and unobserved confounders, using reweighting, optimal transport, and balanced representation learning.

causal inferencedose-responsemachine learning
0 likes · 19 min read
How to Estimate Long‑Term Heterogeneous Dose‑Response Curves with Unobserved Confounding
Sohu Tech Products
Sohu Tech Products
Jul 17, 2024 · Artificial Intelligence

How Weak Supervision Powers Ant Group’s Real‑World AI Challenges

This article presents a comprehensive technical overview of weak‑supervision machine learning at Ant Group, covering its fundamentals, cross‑domain causal effect estimation, strategies for scarce or noisy labels, novel framework components, experimental validation, and practical application scenarios.

AIWeak Supervisioncausal inference
0 likes · 18 min read
How Weak Supervision Powers Ant Group’s Real‑World AI Challenges
DataFunSummit
DataFunSummit
Feb 23, 2023 · Artificial Intelligence

An Introduction to Causal Inference: Concepts, Methods, and Real‑World Applications

This article provides a comprehensive overview of causal inference, explaining its definition, the distinction between correlation and causation, classic pitfalls such as Simpson's paradox, key metrics like ATE and ATT, experimental designs, bias mitigation techniques, and practical case studies from content platforms and the Titanic dataset.

A/B testingbias mitigationcausal inference
0 likes · 22 min read
An Introduction to Causal Inference: Concepts, Methods, and Real‑World Applications
DataFunTalk
DataFunTalk
Dec 26, 2022 · Artificial Intelligence

A Review of Causal Inference Methods: Potential Outcomes, Structural Causal Models, and Recent Advances

This article reviews the two main streams of causal inference—potential‑outcome (Rubin) models and structural causal (Pearl) diagrams—covers classic techniques such as A/B testing, instrumental variables, matching, difference‑in‑differences, synthetic controls, matrix completion, heterogeneous treatment effect estimation, and discusses modern machine‑learning‑based approaches and causal discovery algorithms.

A/B testingcausal inferenceeconometrics
0 likes · 33 min read
A Review of Causal Inference Methods: Potential Outcomes, Structural Causal Models, and Recent Advances
Model Perspective
Model Perspective
Nov 8, 2022 · Fundamentals

Why Causal Relationships Matter: From Prediction to Counterfactuals

Understanding why causal relationships matter reveals the limits of predictive machine learning, introduces counterfactual reasoning, explains potential outcomes, treatment effects, bias, and how to distinguish correlation from causation using simple examples like tablet distribution in schools.

Biascausal inferencecounterfactuals
0 likes · 18 min read
Why Causal Relationships Matter: From Prediction to Counterfactuals
Model Perspective
Model Perspective
Sep 5, 2022 · Fundamentals

Why Understanding Causal Relationships Is Crucial for Machine Learning

This article explains why causal inference matters beyond prediction, introduces potential outcomes notation, demonstrates how bias separates correlation from causation, and outlines the conditions under which observed differences can be interpreted as true causal effects.

BiasPredictioncausal inference
0 likes · 16 min read
Why Understanding Causal Relationships Is Crucial for Machine Learning
DaTaobao Tech
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.

Propensity Score Matchingcausal inferencematching algorithms
0 likes · 13 min read
Propensity Score Matching: Principles, Implementation, and Evaluation
DaTaobao Tech
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.

Methodologycausal inferenceinstrumental variables
0 likes · 24 min read
Introduction to Causal Inference and Instrumental Variables