# Sibling Regression for Generalized Linear Models

arXiv: https://arxiv.org/abs/2107.01338 · HTML: https://mlsanity.com/paper/2107.01338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 3 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [Removing systematic errors for exoplanet search via latent causes](https://mlsanity.com/paper/1505.03036/) (2015)
- [Learning Models from Data with Measurement Error: Tackling Underreporting](https://mlsanity.com/paper/1901.09060/) (2019)
- [Causal inference taking into account unobserved confounding](https://mlsanity.com/paper/1712.00292/) (2017)
- [Necessary and Probably Sufficient Test for Finding Valid Instrumental Variables](https://mlsanity.com/paper/1812.01412/) (2018)
- [Learning Compact Neural Networks Using Ordinary Differential Equations as Activation Functions](https://mlsanity.com/paper/1905.07685/) (2019)
- [Three-quarter Sibling Regression for Denoising Observational Data](https://mlsanity.com/paper/2101.00074/) (2020)

## Abstract (arXiv)

Field observations form the basis of many scientific studies, especially in ecological and social sciences. Despite efforts to conduct such surveys in a standardized way, observations can be prone to systematic measurement errors. The removal of systematic variability introduced by the observation process, if possible, can greatly increase the value of this data. Existing non-parametric techniques for correcting such errors assume linear additive noise models. This leads to biased estimates when applied to generalized linear models (GLM). We present an approach based on residual functions to address this limitation. We then demonstrate its effectiveness on synthetic data and show it reduces systematic detection variability in moth surveys.