# Valid causal inference with unobserved confounding in high-dimensional settings

arXiv: https://arxiv.org/abs/2401.06564 · HTML: https://mlsanity.com/paper/2401.06564/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations](https://mlsanity.com/paper/1309.4686/) (2013)
- [Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap](https://mlsanity.com/paper/1711.11286/) (2017)
- [Flexible sensitivity analysis for observational studies without observable implications](https://mlsanity.com/paper/1809.00399/) (2018)
- [Sensitivity analysis via the proportion of unmeasured confounding](https://mlsanity.com/paper/1912.02793/) (2019)
- [Semiparametric sensitivity analysis: unmeasured confounding in observational studies](https://mlsanity.com/paper/2104.08300/) (2021)
- [hdm: High-Dimensional Metrics](https://mlsanity.com/paper/1608.00354/) (2016)
- [Ultra-high Dimensional Variable Selection for Doubly Robust Causal Inference](https://mlsanity.com/paper/2007.14190/) (2020)
- [Nonparametric bounds for causal effects in imperfect randomized experiments](https://mlsanity.com/paper/2010.05220/) (2020)
- [The costs and benefits of uniformly valid causal inference with high-dimensional nuisance parameters](https://mlsanity.com/paper/2105.02071/) (2021)
- [Causal inference taking into account unobserved confounding](https://mlsanity.com/paper/1712.00292/) (2017)
- [A Semiparametric Approach to Model-based Sensitivity Analysis in Observational Studies](https://mlsanity.com/paper/1910.14130/) (2019)
- [Convolutional neural networks for valid and efficient causal inference](https://mlsanity.com/paper/2301.11732/) (2023)

## Abstract (arXiv)

Various methods have recently been proposed to estimate causal effects with confidence intervals that are uniformly valid over a set of data generating processes when high-dimensional nuisance models are estimated by post-model-selection or machine learning estimators. These methods typically require that all the confounders are observed to ensure identification of the effects. We contribute by showing how valid semiparametric inference can be obtained in the presence of unobserved confounders and high-dimensional nuisance models. We propose uncertainty intervals which allow for unobserved confounding, and show that the resulting inference is valid when the amount of unobserved confounding is small relative to the sample size; the latter is formalized in terms of convergence rates. Simulation experiments illustrate the finite sample properties of the proposed intervals and investigate an alternative procedure that improves the empirical coverage of the intervals when the amount of unobserved confounding is large. Finally, a case study on the effect of smoking during pregnancy on birth weight is used to illustrate the use of the methods introduced to perform a sensitivity analysis to unobserved confounding.