A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome.

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Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, background): “Sensitivity analysis methods that evaluate the potential impact that an unmeasured confounder could have on the estimation of treatment effects have also been developed (Robins et al., 2000b; Roy et al., 2016; Scharfstein et al., 2018).”
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “Roy et al. (2016) use Dirichlet and Gaussian processes to model the observational data and estimate the IPTW in Marginal Structural Models.”