From Bias to Fairness: De‑biasing Techniques in Uplift Modeling

This article explores the fundamentals and challenges of uplift modeling, explains why unbiased random data are essential, and presents a comprehensive suite of bias‑correction methods—including reweighting, propensity‑score matching, and advanced deep‑learning architectures such as TarNet, CFRNet, and DragonNet—to improve causal effect estimation in marketing and finance applications.

Bias CorrectionDeep LearningUplift Modeling

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From Bias to Fairness: De‑biasing Techniques in Uplift Modeling

Model Perspective

Aug 4, 2023 · Fundamentals

Unlock Causal Insights with Python: A Practical Guide to the causalinference Package

This article introduces the Python causalinference library, explains its core CausalModel interface and key methods for propensity‑score estimation, trimming, stratification, and various treatment‑effect estimators, and demonstrates how to interpret the resulting statistical outputs.

CausalModelPythoncausal inference

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Unlock Causal Insights with Python: A Practical Guide to the causalinference Package

DataFunTalk

Jan 20, 2023 · Artificial Intelligence

Practice of Causal Inference Based on Representation Learning: RCT Standards, Joint Tree‑Neural Modeling, RCT‑ODB Fusion, and Feature Decomposition

This article presents a comprehensive industrial‑level guide to causal inference using representation learning, covering proper RCT experiment design, joint modeling of tree and neural networks, fusion of RCT with observational data, and advanced feature‑decomposition techniques to mitigate bias.

Feature DecompositionRCTpropensity score

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Practice of Causal Inference Based on Representation Learning: RCT Standards, Joint Tree‑Neural Modeling, RCT‑ODB Fusion, and Feature Decomposition

Alimama Tech

Jul 28, 2021 · Product Management

Offline Sampling in AB Testing: Challenges and Experimental Techniques

Offline sampling in A/B testing assigns experimental units such as users or tags before a trial begins, but suffers from limited sample size, high heterogeneity, and non‑random allocation, which can be mitigated by variance‑reduction methods like CUPED, stratified sampling with inverse‑probability weighting, and matching approaches including propensity‑score matching.

causal inferenceoffline samplingpropensity score

0 likes · 15 min read

Offline Sampling in AB Testing: Challenges and Experimental Techniques