# On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives

arXiv: https://arxiv.org/abs/1902.10286 · HTML: https://mlsanity.com/paper/1902.10286/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 20 of 20)

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- [Consistent End-to-End Estimation for Counterfactual Fairness](https://mlsanity.com/paper/2310.17687/) (2023)
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- [Deep Causal Reasoning for Recommendations](https://mlsanity.com/paper/2201.02088/) (2022)
- [The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation](https://mlsanity.com/paper/2309.17283/) (2023)
- [Controlling for discrete unmeasured confounding in nonlinear causal models](https://mlsanity.com/paper/2408.05647/) (2024)
- [Identification of Latent Variables From Graphical Model Residuals](https://mlsanity.com/paper/2101.02332/) (2021)
- [Do-calculus enables estimation of causal effects in partially observed biomolecular pathways](https://mlsanity.com/paper/2102.06626/) (2021)
- [Multi-treatment Effect Estimation from Biomedical Data](https://mlsanity.com/paper/2112.07574/) (2021)
- [A/B testing under Interference with Partial Network Information](https://mlsanity.com/paper/2404.10547/) (2024)
- [Shrinkage priors for Bayesian Substitute Confounders](https://mlsanity.com/paper/2606.18535/) (2026)
- [Estimating the Effect of Timing on Coupon Effectiveness](https://mlsanity.com/paper/2606.30664/) (2026)
- [Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference](https://mlsanity.com/paper/2608.01352/) (2026)

## Abstract (arXiv)

Unobserved confounding is a central barrier to drawing causal inferences from observational data. Several authors have recently proposed that this barrier can be overcome in the case where one attempts to infer the effects of several variables simultaneously. In this paper, we present two simple, analytical counterexamples that challenge the general claims that are central to these approaches. In addition, we show that nonparametric identification is impossible in this setting. We discuss practical implications, and suggest alternatives to the methods that have been proposed so far in this line of work: using proxy variables and shifting focus to sensitivity analysis.