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Our upcoming event for the Statistics Department Colloquium Series is scheduled for Monday, November 6 from 12:00 – 1:00pm (ET) and will be an in-person presentation Science Center Rm. 316. Lunch will be provided to guests following the talk. This week's speaker will be Rahul Singh of the Economics Department at Harvard University.
Title: Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy
Abstract: 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 finite sample arguments, with a rate of n^{-1/2} for semiparametric estimands that degrades gracefully for nonparametric estimands. Our key assumption is that the true covariates are approximately low rank, which we interpret as approximate repeated measurements and validate in the Census. In our analysis, we provide nonasymptotic theoretical contributions to matrix completion, statistical learning, and semiparametric statistics. Calibrated simulations verify the coverage of our data cleaning-adjusted confidence intervals and demonstrate the relevance of our results for 2020 Census data.
About: Rahul Singh is a Junior Fellow at the Harvard Society of Fellows. Subsequently, he will be an Assistant Professor at the Harvard Department of Economics. His research interests include econometrics, causal inference, and machine learning.