A Potential Tale of Two by Two Tables from Completely Randomized Experiments
Statistics Theory
2015-01-13 v1 Statistics Theory
Abstract
Causal inference in completely randomized treatment-control studies with binary outcomes is discussed from Fisherian, Neymanian and Bayesian perspectives, using the potential outcomes framework. A randomization-based justification of Fisher's exact test is provided. Arguing that the crucial assumption of constant causal effect is often unrealistic, and holds only for extreme cases, some new asymptotic and Bayesian inferential procedures are proposed. The proposed procedures exploit the intrinsic non-additivity of unit-level causal effects, can be applied to linear and non-linear estimands, and dominate the existing methods, as verified theoretically and also through simulation studies.
Cite
@article{arxiv.1501.02389,
title = {A Potential Tale of Two by Two Tables from Completely Randomized Experiments},
author = {Peng Ding and Tirthankar Dasgupta},
journal= {arXiv preprint arXiv:1501.02389},
year = {2015}
}