A research team from MIT proposes a unified framework for estimation and inference in the presence of various forms of economic data corruption such as measurement errors, missing values, discretization, and differential privacy.

Here is a quick read: MIT Proposes Novel End-to-End Procedure for Corrupted Data Cleaning, Estimation, and Inference.

The paper Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy is on arXiv.

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