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Performing causal inference in observational studies requires we assumeconfounding variables are correctly adjusted for. G-computation methods areoften used in these scenarios, with several recent proposals using Bayesianversions of g-computation. In settings with few confounders, standard modelscan be employed, however as the number of confounders increase these modelsbecome less feasible as there are fewer observations available for each uniquecombination of confounding variables. In this paper we propose a new model forestimating treatment effects in observational studies that incorporates bothparametric and nonparametric outcome models. By conceptually splitting thedata, we can combine these models while maintaining a conjugate framework,allowing us to avoid the use of MCMC methods. Approximations using the centrallimit theorem and random sampling allows our method to be scaled to highdimensional confounders while maintaining computational efficiency. Weillustrate the model using carefully constructed simulation studies, as well ascompare the computational costs to other benchmark models.

Combining Parametric and Nonparametric Models to Estimate Treatment Effects in Observational Studies