Good practices for quantitative bias analysis.

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Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “Alternative methods for dealing with hidden confounders in the static setting involve using proxy variables as noisy substitutes for latent confounders (Lash et al., 2014; Kuroki & Pearl, 2014; Louizos et al., 2017; Lee et al., 2018).”
From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges (2020, background): “Alternatively, several methods have been proposed to make use of proxy variables 42–46 or the presence of multiple treatments 47–50 to infer latent variables that can act as substitutes for the hidden confounders.”
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations (2016, background): “These assumptions have gradually come under scrutiny via sensitivity and bias analysis (e.g., Lash et al. 2014), but such meth-ods remain far removed from the basic statistical training given to most researchers.”