Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy. (arXiv:2107.02780v5 [econ.EM] UPDATED)

The US Census Bureau will deliberately corrupt data sets derived from the
2020 US Census in an effort to maintain privacy, suggesting a painful trade-off
between the privacy of respondents and the precision of economic analysis. To
investigate whether this trade-off is inevitable, we formulate a semiparametric
model of causal inference with high dimensional corrupted data. We propose a
procedure for data cleaning, estimation, and inference with data
cleaning-adjusted confidence intervals. We prove consistency, Gaussian
approximation, and semiparametric efficiency by …

arxiv causal inference data differential privacy error inference measurement missing values privacy values