# Model interpretation through lower-dimensional posterior summarization

arXiv: https://arxiv.org/abs/1905.07103 · HTML: https://mlsanity.com/paper/1905.07103/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 5 of 5)

- [Bayesian subset selection and variable importance for interpretable prediction and classification](https://mlsanity.com/paper/2104.10150/) (2021)
- [A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding](https://mlsanity.com/paper/2402.02306/) (2024)
- [Bayesian Additive Distribution Regression](https://mlsanity.com/paper/2603.06462/) (2026)
- [Nowcasting with Mixed Frequency Data Using Gaussian Processes](https://mlsanity.com/paper/2402.10574/) (2024)
- [Bayesian fusion forests for heterogeneous treatment effects on survival from randomised and real-world data](https://mlsanity.com/paper/2607.29295/) (2026)

## Abstract (arXiv)

Nonparametric regression models have recently surged in their power and popularity, accompanying the trend of increasing dataset size and complexity. While these models have proven their predictive ability in empirical settings, they are often difficult to interpret and do not address the underlying inferential goals of the analyst or decision maker. In this paper, we propose a modular two-stage approach for creating parsimonious, interpretable summaries of complex models which allow freedom in the choice of modeling technique and the inferential target. In the first stage a flexible model is fit which is believed to be as accurate as possible. In the second stage, lower-dimensional summaries are constructed by projecting draws from the distribution onto simpler structures. These summaries naturally come with valid Bayesian uncertainty estimates. Further, since we use the data only once to move from prior to posterior, these uncertainty estimates remain valid across multiple summaries and after iteratively refining a summary. We apply our method and demonstrate its strengths across a range of simulated and real datasets. Code to reproduce the examples shown is avaiable at github.com/spencerwoody/ghost