# On randomization-based causal inference for matched-pair factorial designs

## TL;DR

This paper introduces matched-pair factorial designs within the potential outcomes framework, proposing estimators for factorial effects and their covariance, enhancing causal inference methods for complex experimental setups.

## Contribution

It develops a new matched-pair design framework and provides estimators for factorial effects and their covariance matrices under randomization-based causal inference.

## Key findings

- Derived the matched-pair estimator for factorial effects
- Calculated the covariance matrix of the estimator
- Provided a Neymanian estimator for the covariance matrix

## Abstract

Under the potential outcomes framework, we introduce matched-pair factorial designs, and propose the matched-pair estimator of the factorial effects. We also calculate the randomization-based covariance matrix of the matched-pair estimator, and provide the "Neymanian" estimator of the covariance matrix.