Inference for Low-rank Completion without Sample Splitting with Application to Treatment Effect Estimation

Jungjun Choi; Hyukjun Kwon; Yuan Liao

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arXiv · 2307.16370

Inference for Low-rank Completion without Sample Splitting with Application to Treatment Effect Estimation

Jungjun Choi·Hyukjun Kwon·Yuan Liao

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Abstract

This paper studies the inferential theory for estimating low-rank matrices. It also provides an inference method for the average treatment effect as an application. We show that the least square estimation of eigenvectors following the nuclear norm penalization attains the asymptotic normality. The key contribution of our method is that it does not require sample splitting. In addition, this paper allows dependent observation patterns and heterogeneous observation probabilities. Empirically, we apply the proposed procedure to estimating the impact of the presidential vote on allocating the U.S. federal budget to the states.

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BibTeX RIS

Jungjun Choi, Hyukjun Kwon, Yuan Liao. 2023-07-31. Inference for Low-rank Completion without Sample Splitting with Application to Treatment Effect Estimation. https://arxiv.org/abs/2307.16370

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