# Effect Inference from Two-Group Data with Sampling Bias

## TL;DR

This paper introduces a new inference method that accurately compares two populations despite sampling biases, maintaining low false positive rates where standard methods fail.

## Contribution

The authors develop a bias-resilient inference technique that controls false positives under moderate sampling biases, improving reliability over traditional methods.

## Key findings

- Method performs well on synthetic data
- Effective on real biomarker datasets
- Reduces false positives under bias

## Abstract

In many applications, different populations are compared using data that are sampled in a biased manner. Under sampling biases, standard methods that estimate the difference between the population means yield unreliable inferences. Here we develop an inference method that is resilient to sampling biases and is able to control the false positive errors under moderate bias levels in contrast to the standard approach. We demonstrate the method using synthetic and real biomarker data.