  A framework for causal inference from two-level factorial designs is
proposed. The framework utilizes the concept of potential outcomes that lies at
the center stage of causal inference and extends Neyman's repeated sampling
approach for estimation of causal effects and randomization tests based on
Fisher's sharp null hypothesis to the case of 2-level factorial experiments.
The framework allows for statistical inference from a finite population,
permits definition and estimation of estimands other than "average factorial
effects" and leads to more flexible inference procedures than those based on
ordinary least squares estimation from a linear model.
