Improved Neymanian analysis for 2^K factorial designs with binary outcomes

2018/03/12 by Jiannan Lu, Lu, Jiannan
Mathematics · Decision Sciences · #Statistical Methods and Inference #Optimal Experimental Design Methods #Advanced Causal Inference Techniques

paper · pdf · doi:10.48550/arxiv.1803.04503

Abstract

2^K factorial designs are widely adopted by statisticians and the broader scientific community. In this short note, under the potential outcomes framework (Neyman, 1923; Rubin, 1974), we adopt the partial identification approach and derive the sharp lower bound of the sampling variance of the estimated factorial effects, which leads to an "improved" Neymanian variance estimator that mitigates the over-estimation issue suffered by the classic Neymanian variance estimator by Dasgupta et al. (2015).

Citations

• On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9
• Estimating causal effects of treatments in randomized and nonrandomized studies.

Related

• Sharpening randomization-based causal inference for 2² factorial designs with binary outcomes
• Causal inference from 2^k factorial designs using the potential outcomes model
• On Randomization-based and Regression-based Inferences for 2^K Factorial Designs
• On randomization-based causal inference for matched-pair factorial designs
• Neymanian inference in randomized experiments