
TL;DR
This paper develops methods for causal inference with many noisy proxy controls, exploiting low-rank and sparsity structures to identify and estimate confounders in high-dimensional settings.
Contribution
It introduces a novel approach that leverages rank and sparsity to estimate unobserved confounders with many proxies, including a closed-form and doubly-robust estimator.
Findings
The proposed estimators are consistent and asymptotically normal.
They outperform existing methods in high-dimensional simulations.
The approach effectively handles unknown number of confounders.
Abstract
A recent literature considers causal inference using noisy proxies for unobserved confounding factors. The proxies are divided into two sets that are independent conditional on the confounders. One set of proxies are `negative control treatments' and the other are `negative control outcomes'. Existing work applies to low-dimensional settings with a fixed number of proxies and confounders. In this work we consider linear models with many proxy controls and possibly many confounders. A key insight is that if each group of proxies is strictly larger than the number of confounding factors, then a matrix of nuisance parameters has a low-rank structure and a vector of nuisance parameters has a sparse structure. We can exploit the rank-restriction and sparsity to reduce the number of free parameters to be estimated. The number of unobserved confounders is not known a priori but we show that it…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Inference · Bayesian Modeling and Causal Inference
