Time-modified confounding.

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Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “MSMs compute propensity weights to construct a pseudo-population from the observational data that resembles the one in a clinical trial and thus remove the selection bias and the bias introduced by time-dependent confounders (Platt et al., 2009).”
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, background): “The biggest challenge when estimating the effects of time-dependent treatments from observational data involves correctly handling the time-dependent confounders: patient covariates that are affected by past treatments which then influence future treatments and outcomes (Platt et al., 2009).”