# Towards Clarifying the Theory of the Deconfounder

arXiv: https://arxiv.org/abs/2003.04948 · HTML: https://mlsanity.com/paper/2003.04948/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [Desiderata for Representation Learning: A Causal Perspective](https://mlsanity.com/paper/2109.03795/) (2021)
- [Sequential Deconfounding for Causal Inference with Unobserved Confounders](https://mlsanity.com/paper/2104.09323/) (2021)
- [Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders](https://mlsanity.com/paper/2303.02320/) (2023)
- [Mitigating Health Disparities in EHR via Deconfounder](https://mlsanity.com/paper/2210.15901/) (2022)
- [Estimating Social Influence from Observational Data](https://mlsanity.com/paper/2204.01633/) (2022)
- [Deep Causal Reasoning for Recommendations](https://mlsanity.com/paper/2201.02088/) (2022)
- [Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders](https://mlsanity.com/paper/2211.09072/) (2022)
- [A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder](https://mlsanity.com/paper/2308.11819/) (2023)
- [Substitute adjustment via recovery of latent variables](https://mlsanity.com/paper/2403.00202/) (2024)

## Abstract (arXiv)

Wang and Blei (2019) studies multiple causal inference and proposes the deconfounder algorithm. The paper discusses theoretical requirements and presents empirical studies. Several refinements have been suggested around the theory of the deconfounder. Among these, Imai and Jiang clarified the assumption of "no unobserved single-cause confounders." Using their assumption, this paper clarifies the theory. Furthermore, Ogburn et al. (2020) proposes counterexamples to the theory. But the proposed counterexamples do not satisfy the required assumptions.