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In recommender systems, the collected data used for training is always subject to selection bias, which poses a great challenge for unbiased learning.

A Introduction of do calculus. Do-calculus consists of three rules that help with identifying causal effects. Intuitively, Rule A.1 states when an observant can be omitted in estimating the interventional Theorem B.2. Suppose that the latent variable They assume that confounders exist but they are unobservable. Adapting C-Disentanglement to existing works further improve their performance.

Representation learning assumes that real-world data is generated by a few semantically meaningful generative factors (i.e., sources of variation) and aims to

Valid causal inference in observational studies often requires controlling for confounders. However, in practice measurements of confounders may be noisy, and can lead to biased estimates of causal effects. We show that we can reduce bias induced by measurement noise using a large number of noisy measurements of the underlying confounders. We propose the use of matrix factorization to infer the confounders from noisy covariates. This flexible and principled framework adapts to missing values, accommodates a wide variety of data types, and can enhance a wide variety of causal inference methods. We bound the error for the induced average treatment effect estimator and show it is consistent in a linear regression setting, using Exponential Family Matrix Completion preprocessing. We demonstrate the effectiveness of the proposed procedure in numerical experiments with both synthetic data and real clinical data.