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Rahul Singh

Job Market Candidate

Research Fields

Econometrics

Contact Information

Phone 216-213-1293
Email Address rahul.singh@mit.edu
Research Fields Econometrics Causal Inference Machine Learning

Job Market Paper

Causal inference with corrupted data: Measurement error, missing values, discretization, and differential privacy 

(with Anish Agarwal)

arXiv: 2107.02780, 2021. Extended abstract in NeurIPS Workshop on Machine Learning Meets Econometrics, 2021 [talk]

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.


Publications

Automatic debiased machine learning of causal and structural effects

(with Victor Chernozhukov and Whitney K. Newey)

Econometrica, 2022

A simple and general debiased machine learning theorem with finite-sample guarantees

(with Victor Chernozhukov and Whitney K. Newey)

Biometrika, 2022

Debiased machine learning of global and local parameters using regularized Riesz representers

(with Victor Chernozhukov and Whitney K. Newey)

The Econometrics Journal, 2022

Kernel instrumental variable regression

(first author; with Maneesh Sahani and Arthur Gretton)

NeurIPS, 2019 (Oral presentation; 0.5% acceptance rate) [talk]


Under Review

Kernel methods for unobserved confounding: Negative controls, proxies, and instruments

arXiv: 2012.10315, 2020. Revised & resubmitted to Journal of the American Statistical Association: Theory & Methods

Double robustness for complier parameters and a semiparametric test for complier characteristics

(with Liyang Sun)

arXiv: 1909.05244, 2019. Extended abstract in NeurIPS Workshop on Causal Machine Learning, 2019 (Spotlight presentation). Submitted

Kernel methods for causal functions: Dose, heterogeneous, and incremental response curves

(first author; with Liyuan Xu and Arthur Gretton)

arXiv: 2010.04855, 2020. Extended abstract in NeurIPS Workshop on Machine Learning for Economic Policy, 2020. Submitted

Kernel methods for multistage causal inference: Mediation analysis and dynamic treatment effects

(first author; with Liyuan Xu and Arthur Gretton)

arXiv: 2111.03950, 2021. Extended abstract in NeurIPS Workshop on Causal Sequential Decisions, 2021. Submitted


Working Papers

A finite sample theorem for longitudinal causal inference with machine learning: Long term, dynamic, and mediated effects

arXiv: 2112.14249, 2021

Adversarial estimation of Riesz representers

(with Victor Chernozhukov, Whitney K. Newey, and Vasilis Syrgkanis)

arXiv: 2101.00009, 2020. Extended abstract in ICML Workshop on New Frontiers in Adversarial Machine Learning, 2022

Automatic debiased machine learning for dynamic treatment effects and general nested functionals

(with Victor Chernozhukov, Whitney K. Newey, and Vasilis Syrgkanis)

arXiv: 2203.13887, 2022


Selected Works in Progress

Debiased kernel methods

arXiv: 2102.11076, 2021

Kernel methods for attrition bias

arXiv: 2111.05277, 2021

Kernel methods for long term causal inference

arXiv: 2201.05139, 2022 [talk]

Selected Fellowships

2022
Simons-Berkeley Research Fellowship
2017
MIT Presidential Fellowship
2015
Marshall Scholarship