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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.
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.
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.