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.
causal inferencedeconfoundingmachine learning
0 likes · 22 min read