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

TL;DR

This paper introduces a novel hybrid modeling approach that combines parametric and nonparametric methods for estimating treatment effects in observational studies, enhancing scalability and computational efficiency in high-dimensional confounder settings.

Contribution

The paper proposes a new conjugate framework that integrates parametric and nonparametric outcome models, avoiding MCMC and enabling scalable causal inference with many confounders.

Findings

The method performs well in simulation studies.

It reduces computational costs compared to benchmark models.

It maintains accuracy in high-dimensional confounder scenarios.

Abstract

Performing causal inference in observational studies requires we assume confounding variables are correctly adjusted for. G-computation methods are often used in these scenarios, with several recent proposals using Bayesian versions of g-computation. In settings with few confounders, standard models can be employed, however as the number of confounders increase these models become less feasible as there are fewer observations available for each unique combination of confounding variables. In this paper we propose a new model for estimating treatment effects in observational studies that incorporates both parametric and nonparametric outcome models. By conceptually splitting the data, we can combine these models while maintaining a conjugate framework, allowing us to avoid the use of MCMC methods. Approximations using the central limit theorem and random sampling allows our method to be scaled to high dimensional confounders while maintaining computational efficiency. We illustrate the model using carefully constructed simulation studies, as well as compare the computational costs to other benchmark models.