Fetching the paper…
Reading the bibliography…
The US Census Bureau will deliberately corrupt data sets derived from the 2020 US Census, enhancing the privacy of respondents while potentially reducing the precision of economic analysis.
Matrix completion, counterfactuals, and factor analysis of missing data
Bai, J. and Ng, S. (2019) · 1910
Earlier work this paper cites.
Inference and missing data
Rubin, D. B. (1976) · 1976
Earlier work this paper cites.
On the nonparametric estimation of functionals
Hasminskii, R. Z. and Ibragimov, I. A. (1979) · 1979
Earlier work this paper cites.
Consistent estimation of the influence function of locally asymptotically linear estimators
Klaassen, C. A. J. (1987) · 1987
Earlier work this paper cites.
Root- n n -consistent semiparametric regression
Robinson, P. M. (1988) · 1988
Earlier work this paper cites.
Identification and estimation of polynomial errors-in-variables models
Hausman, J. A., Newey, W. K., Ichimura, H., and Powell, J. L. (1991) · 1991
Earlier work this paper cites.
Efficient and Adaptive Estimation for Semiparametric Models
Bickel, P. J., Klaassen, C. A. J., Ritov, Y., and Wellner, J. A. (1993) · 1993
Earlier work this paper cites.
Asymptotics for semiparametric econometric models via stochastic equicontinuity
Andrews, D. W. K. (1994) · 1994
Earlier work this paper cites.
The asymptotic variance of semiparametric estimators
Newey, W. K. (1994) · 1994
Earlier work this paper cites.
Semiparametric efficiency in multivariate regression models with missing data
Robins, J. M. and Rotnitzky, A. (1995) · 1995
Earlier work this paper cites.
Nonparametric estimation of the measurement error model using multiple indicators
Li, T. and Vuong, Q. (1998) · 1998
Earlier work this paper cites.
Determining the number of factors in approximate factor models
Bai, J. and Ng, S. (2002) · 2002
Earlier work this paper cites.
Efficient estimation of models with conditional moment restrictions containing unknown functions
Ai, C. and Chen, X. (2003) · 2003
Earlier work this paper cites.
Inferential theory for factor models of large dimensions
Bai, J. (2003) · 2003
Earlier work this paper cites.
Estimation of nonlinear models with measurement error
Schennach, S. M. (2004) · 2004
Earlier work this paper cites.
Agarwal, A., Shah, D., and Shen, D. (2020b) · 2006
Earlier work this paper cites.
Confidence intervals for diffusion index forecasts and inference for factor-augmented regressions
Bai, J. and Ng, S. (2006) · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006) · 2006
Earlier work this paper cites.
Identification and formal privacy guarantees
Komarova, T. and Nekipelov, D. (2020) · 2006
Earlier work this paper cites.
Targeted maximum likelihood learning
Van der Laan, M. J. and Rubin, D. (2006) · 2006
Earlier work this paper cites.
Instrumental variable estimation of nonlinear errors-in-variables models
Schennach, S. M. (2007) · 2007
Earlier work this paper cites.
Causal inference in possibly nonlinear factor models
Feng, Y. (2020) · 2008
Earlier work this paper cites.
Instrumental variable treatment of nonclassical measurement error models
Hu, Y. and Schennach, S. M. (2008) · 2008
Cited alongside, same era.
Exact matrix completion via convex optimization
Candès, E. J. and Recht, B. (2009) · 2009
Cited alongside, same era.
Differential privacy and robust statistics
Dwork, C. and Lei, J. (2009) · 2009
Cited alongside, same era.
Matrix completion from noisy entries
Keshavan, R., Montanari, A., and Oh, S. (2009) · 2009
Cited alongside, same era.
Testing hypotheses about the number of factors in large factor models
Onatski, A. (2009) · 2009
Cited alongside, same era.
Agarwal, A., Shah, D., and Shen, D. (2020a) · 2010
Double/debiased machine learning for treatment and structural parameters
Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W. K., and Robins, J. (2018) · 2018
Later among the works it cites.
Deaner, B. (2018) · 2018
Later among the works it cites.
Causal inference with noisy and missing covariates via matrix factorization
Kallus, N., Mao, X., and Udell, M. (2018) · 2018
Later among the works it cites.
Identification, data combination, and the risk of disclosure
Komarova, T., Nekipelov, D., and Yakovlev, E. (2018) · 2018
Later among the works it cites.
Identifying causal effects with proxy variables of an unmeasured confounder
Miao, W., Geng, Z., and Tchetgen Tchetgen, E. J. (2018) · 2018
Later among the works it cites.
Targeted Learning in Data Science
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
The Census Bureau’s simulated reconstruction-abetted re-identification attack on the 2010 Census
Hawes, M. (2021) · 2010
Cited alongside, same era.
An improvement of convergence rate estimates in the Lyapunov theorem
Shevtsova, I. G. (2010) · 2010
Cited alongside, same era.
Privacy-preserving statistical estimation with optimal convergence rates
Smith, A. (2011) · 2011
Cited alongside, same era.
Cross-Validated Targeted Minimum-Loss-Based Estimation
Zheng, W. and Van der Laan, M. J. (2011) · 2011
Cited alongside, same era.
High-dimensional regression with noisy and missing data: Provable guarantees with nonconvexity
Loh, P.-L. and Wainwright, M. J. (2012) · 2012
Cited alongside, same era.
The China syndrome: Local labor market effects of import competition in the United States
Autor, D. H., Dorn, D., and Hanson, G. H. (2013) · 2013
Cited alongside, same era.
Van der Laan, M. J. and Rose, S. (2018) · 2018
Later among the works it cites.
High-Dimensional Probability: An Introduction with Applications in Data Science
Vershynin, R. (2018) · 2018
Later among the works it cites.
An economic analysis of privacy protection and statistical accuracy as social choices
Abowd, J. M. and Schmutte, I. M. (2019) · 2019
Later among the works it cites.
A practical method to reduce privacy loss when disclosing statistics based on small samples
Chetty, R. and Friedman, J. N. (2019) · 2019
Later among the works it cites.
The 2020 census disclosure avoidance system TopDown algorithm
Abowd, J. M., Ashmead, R., Cumings-Menon, R., Garfinkel, S., Heineck, M., Heiss, C., Johns, R., Kifer, D., Leclerc, P., Machanavajjhala, A., Moran, B., Secton, W., Spence, M., and Zhuravlev, P. (2022) · 2020
Later among the works it cites.
On robustness of principal component regression
Agarwal, A., Shah, D., Shen, D., and Song, D. (2021) · 2021
Closest in time.
Matrix completion methods for causal panel data models
Athey, S., Bayati, M., Doudchenko, N., Imbens, G., and Khosravi, K. (2021) · 2021
Closest in time.
The balancing act in causal inference
Ben-Michael, E., Feller, A., Hirshberg, D. A., and Zubizarreta, J. R. (2021) · 2021
Closest in time.
Low-rank approximations of nonseparable panel models
Fernández-Val, I., Freeman, H., and Weidner, M. (2021) · 2021
Closest in time.
Augmented minimax linear estimation
Hirshberg, D. A. and Wager, S. (2021) · 2021
Closest in time.
Characterization of parameters with a mixed bias property
Rotnitzky, A., Smucler, E., and Robins, J. M. (2021) · 2021
Closest in time.
Balancing data privacy and usability in the federal statistical system
Hotz, V. J., Bollinger, C. R., Komarova, T., Manski, C. F., Moffitt, R. A., Nekipelov, D., Sojourner, A., and Spencer, B. D. (2022) · 2022
Closest in time.
Policy impacts of statistical uncertainty and privacy
Steed, R., Liu, T., Wu, Z. S., and Acquisti, A. (2022) · 2022
Closest in time.
A simple and general debiased machine learning theorem with finite-sample guarantees
Chernozhukov, V., Newey, W. K., and Singh, R. (2023) · 2023
Closest in time.
Large dimensional latent factor modeling with missing observations and applications to causal inference
Xiong, R. and Pelger, M. (2023) · 2023
Closest in time.
The power of convex relaxation: Near-optimal matrix completion
Candès, E. J. and Tao, T. (2010) · 2080
Closest in time.