Towards Clarifying the Theory of the Deconfounder
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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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Mar 2020.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 2; 2024: 1.
9 papers.
- 2021Desiderata for Representation Learning: A Causal Perspectivecited by 76
- 2021Sequential Deconfounding for Causal Inference with Unobserved Confounderscited by 24
- 2023Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounderscited by 17
- 2022Mitigating Health Disparities in EHR via Deconfoundercited by 7
- 2022Estimating Social Influence from Observational Datacited by 5
- 2022Deep Causal Reasoning for Recommendationscited by 1
- 2022Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounderscited by 1
- 2023A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfoundercited by 1
- 2024Substitute adjustment via recovery of latent variablescited by 1
- 2024Substitute adjustment via recovery of latent variables1 Mar 2024
- 2023A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfounder22 Aug 2023
- 2023Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders4 Mar 2023
- 2022Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders16 Nov 2022
- 2022Mitigating Health Disparities in EHR via Deconfounder28 Oct 2022
- 2022Estimating Social Influence from Observational Data24 Mar 2022
- 2022Deep Causal Reasoning for Recommendations6 Jan 2022
- 2021Desiderata for Representation Learning: A Causal Perspective8 Sep 2021
- 2021Sequential Deconfounding for Causal Inference with Unobserved Confounders16 Apr 2021