Instrumental variable estimation for compositional treatments.

Area of Science:

• Ecology
• Microbiome Research
• Single-cell Sequencing Data Analysis

Background:

• Many scientific datasets, including species abundances, cell-type compositions, and microbiome amplicon data, are compositional.
• Interpreting causal relationships in compositional data presents unique challenges and potential pitfalls.
• Common statistical measures like diversity indices may lead to misinterpretations of causal effects.

Purpose of the Study:

• To provide a causal perspective on compositional data within an instrumental variable framework.
• To identify and articulate potential misinterpretations of compositional causes, particularly concerning interventions.
• To develop and advocate for robust multivariate methods for valid cause-effect estimation.

Main Methods:

• Articulating pitfalls in interpreting compositional causes from an interventionist viewpoint.
• Developing multivariate statistical methods incorporating data transformations and regression techniques.
• Accounting for the unique structure of the compositional sample space in analysis.

Main Results:

• Demonstrated advantages and limitations of the proposed methods through comparative analysis.
• Highlighted the inadequacy of common summary statistics for causal inference in compositional data.
• Provided a framework for scientifically interpretable results from compositional data.

Conclusions:

• The proposed multivariate methods offer a valid and informative approach to cause-effect estimation for compositional data.
• Practitioners should exercise caution when interpreting causal meanings from summary statistics of compositional data.
• This work provides essential guidance for robust causal inference in fields utilizing compositional datasets.