# On Modeling and Estimation for the Relative Risk and Risk Difference

arXiv: https://arxiv.org/abs/1510.02430 · HTML: https://mlsanity.com/paper/1510.02430/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2015.

## Built on by (most cited first, 3 of 3)

- [Augmented Learning of Heterogeneous Treatment Effects via Gradient Boosting Trees](https://mlsanity.com/paper/2302.01367/) (2023)
- [Staged Event Trees for Transparent Treatment Effect Estimation](https://mlsanity.com/paper/2509.26265/) (2025)
- [Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects](https://mlsanity.com/paper/2605.26288/) (2026)

## Abstract (arXiv)

A common problem in formulating models for the relative risk and risk difference is the variation dependence between these parameters and the baseline risk, which is a nuisance model. We address this problem by proposing the conditional log odds-product as a preferred nuisance model. This novel nuisance model facilitates maximum-likelihood estimation, but also permits doubly-robust estimation for the parameters of interest. Our approach is illustrated via simulations and a data analysis.