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Causal Inference with Noisy and Missing Covariates via Matrix Factorization
Nathan Kallus · Xiaojie Mao · Madeleine Udell

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 210 #6

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.

Author Information

Nathan Kallus (Cornell University)
Xiaojie Mao (Cornell University)
Madeleine Udell (Cornell University)

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