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MIT Proposes Novel End-to-End Procedure for Corrupted Data Cleaning, Estimation, and Inference

Economic data is imperfect, and even the most carefully curated datasets can have noisy, missing, discretized, or privatized variables. Moreover, today’s standard data cleaning procedures often fail to consider the bias and variance consequences of data cleaning, which may mislead efforts at causal inference.

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