When no answer is better than a wrong answer: A causal perspective on batch effects.

Area of Science:

• Data Science
• Biostatistics
• Neuroimaging Analysis

Background:

• Batch effects introduce variability in multi-experiment data, challenging scientific discovery and reproducibility.
• Traditional statistical methods struggle to distinguish batch effects from confounding biases, leading to inaccurate conclusions.
• The reproducibility crisis in science is exacerbated by unaddressed batch effects.

Purpose of the Study:

• To formalize batch effects as causal effects, moving beyond classical statistical modeling.
• To develop and validate algorithms using causal inference for robust batch effect detection.
• To provide a framework for understanding the capabilities and limitations of multi-site data analysis.

Main Methods:

• Formalizing batch effects using causal inference principles.
• Developing novel algorithms leveraging causal machinery for batch effect assessment.
• Conducting simulations and applying methods to real-world neuroimaging datasets.

Main Results:

• Causal methods provide more accurate batch effect identification compared to non-causal approaches.
• When uncertain, causal methods correctly indicate insufficient data rather than forcing a conclusion.
• Application to neuroimaging data revealed discrepancies with non-causal methods, highlighting the benefits of the causal framework.

Conclusions:

• A causal framework offers a more reliable approach to identifying and managing batch effects in complex datasets.
• The proposed methods enhance the accuracy and interpretability of multi-site data analysis.
• This work clarifies the potential and limitations of analyzing data from multiple sources.