# Instrumental Variable Estimation for Compositional Treatments

arXiv: https://arxiv.org/abs/2106.11234 · HTML: https://mlsanity.com/paper/2106.11234/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 71 references that are in the map.
- First posted on arXiv 21 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Anchor regression: heterogeneous data meets causality](https://mlsanity.com/paper/1801.06229/) (2018)
- [Kernel Instrumental Variable Regression](https://mlsanity.com/paper/1906.00232/) (2019)
- [Deep Generalized Method of Moments for Instrumental Variable Analysis](https://mlsanity.com/paper/1905.12495/) (2019)
- [DeepCoDA: personalized interpretability for compositional health data](https://mlsanity.com/paper/2006.01392/) (2020)
- [High-dimensional Log-Error-in-Variable Regression with Applications to Microbial Compositional Data Analysis](https://mlsanity.com/paper/1811.11709/) (2018)
- [Log-ratio Lasso: Scalable, Sparse Estimation for Log-ratio Models](https://mlsanity.com/paper/1709.01139/) (2017)

## Built on by (most cited first, 1 of 1)

- [Sequential Underspecified Instrument Selection for Cause-Effect Estimation](https://mlsanity.com/paper/2302.05684/) (2023)

## Abstract (arXiv)

Many scientific datasets are compositional in nature. Important biological examples include species abundances in ecology, cell-type compositions derived from single-cell sequencing data, and amplicon abundance data in microbiome research. Here, we provide a causal view on compositional data in an instrumental variable setting where the composition acts as the cause. First, we crisply articulate potential pitfalls for practitioners regarding the interpretation of compositional causes from the viewpoint of interventions and warn against attributing causal meaning to common summary statistics such as diversity indices in microbiome data analysis. We then advocate for and develop multivariate methods using statistical data transformations and regression techniques that take the special structure of the compositional sample space into account while still yielding scientifically interpretable results. In a comparative analysis on synthetic and real microbiome data we show the advantages and limitations of our proposal. We posit that our analysis provides a useful framework and guidance for valid and informative cause-effect estimation in the context of compositional data.