Treatment Effects Inference with High-Dimensional Instruments and Control Variables

Xiduo Chen, Xingdong Feng, Antonio Galvao and Yeheng Ge

Papers from arXiv.org

Abstract: Obtaining valid treatment effect inference remains a challenging problem when dealing with numerous instruments and non-sparse control variables. In this paper, we propose a novel ridge regularization-based instrumental variables method for estimation and inference in the presence of both high-dimensional instrumental variables and high-dimensional control variables. These methods are applicable both with and without sparsity assumptions. To remove the estimation bias, we introduce a two-step procedure employing a ridge regression coupled with data-splitting in the first step, and a ridge style projection matrix with a simple least squares regression in the second. We establish statistical properties of the estimator, including consistency and asymptotic normality. Furthermore, we develop practical statistical inference procedures by providing a consistent estimator for the asymptotic variance of the estimator. The finite sample performance of the proposed methods is evaluated through numerical simulations. Results indicate that the new estimator consistently outperforms existing sparsity-based approaches across various settings, offering valuable insights for complex scenarios. Finally, we provide an empirical application estimating the causal effect of schooling on earnings addressing potential endogeneity through the use of high-dimensional instrumental variables and high-dimensional covariates.

Date: 2025-03, Revised 2025-10
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2503.20149 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2503.20149

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().