When it comes to Reco Sys, what are the standard techniques for causal inference ?
Aisha Ejura Dahunsi Nee Ugbenyo
Instrumental Variable (IV) estimation: uses an instrumental variable to identify the causal effect of recommendation on outcome. Propensity Score Matching (PSM): matches users who received recommendations with those who didn't, based on propensity scores. Causal Trees and Forests: uses decision trees and random forests to estimate causal effects. Structural Causal Models (SCMs): represent causal relationships using directed acyclic graphs (DAGs). Causal Effect Estimation using Regression-based methods: uses regression models to estimate causal effects. Uplift Modeling: estimates the causal effect of recommendation on user behavior, while accounting for individual user responses. Causal Impact Estimation: uses Bayesian structural time series models to estimate causal effects.
Causal inference for recommendation systems involves using techniques to estimate the causal effect of recommending an item on user behavior, such as engagement or purchase. Standard techniques include:
These techniques help recommendation systems evaluate the effectiveness of their recommendations and make data-driven decisions.
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