# Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models

arXiv: https://arxiv.org/abs/2408.09634 · HTML: https://mlsanity.com/paper/2408.09634/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Polynomial Regression As an Alternative to Neural Nets](https://mlsanity.com/paper/1806.06850/) (2018)
- [Maximum Entropy Estimation of Heterogeneous Causal Effects](https://mlsanity.com/paper/2407.08862/) (2024)
- [Partial Identification of the Average Treatment Effect with Stochastic Counterfactuals and Discordant Twins](https://mlsanity.com/paper/2407.19057/) (2024)
- [Regression Analysis of Unmeasured Confounding](https://mlsanity.com/paper/1908.08596/) (2019)

## Abstract (arXiv)

It can be difficult to interpret a coefficient of an uncertain model. A slope coefficient of a regression model may change as covariates are added or removed from the model. In the context of high-dimensional data, there are too many model extensions to check. However, as we show here, it is possible to efficiently search, with a branch and bound algorithm, for maximum and minimum values of that adjusted slope coefficient over a discrete space of regularized regression models. Here we introduce our algorithm, along with supporting mathematical results, an example application, and a link to our computer code, to help researchers summarize high-dimensional data and assess the stability of regression coefficients in uncertain models.