On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative

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Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, background): “Moreover, to infer the assigned treatments at See Wang & Blei (2019a) for a longer discussion addressing the concerns in (DAmour, 2019).”
Disentangling Factors of Variation Using Few Labels (2019, background): “Furthermore, by assuming to partially observe all the causal parents of x we avoid unobserved confounding between z and x which makes the structure not identifiable from observational data [49, 11, 60].”
Disentangling Factors of Variations Using Few Labels (2020, background): “Except for very special cases, the recovery of the structure of the generative model is known to be impossible with purely observational data (Peters et al., 2017; D’Amour, 2019; Suter et al., 2019).”
Multi-cause causal inference with unmeasured confounding and binary outcome. (2019, methodology): “However, as pointed out by D’Amour (2019), E{Y (a)} is not necessarily identifiable.”
Using Text Embeddings for Causal Inference (2019, background): “Work on causal inference with hidden confounding and many treatments is in the same vein [ WB18; RP18; D’A19 ] .”
Visual Commonsense R-CNN (2020, background): “Fortunately, this is reasonable: 1) we can consider the objects as the partially observed children of the unobserved confounder [15]; 2) we propose the implementation below to approximate the contexts, e.”