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Causal inference from 2 K factorial designs by using potential outcomes
Author(s) -
Dasgupta Tirthankar,
Pillai Natesh S.,
Rubin Donald B.
Publication year - 2015
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/rssb.12085
Subject(s) - causal inference , inference , factorial , statistics , mathematics , statistical inference , econometrics , null hypothesis , factorial experiment , statistical hypothesis testing , ordinary least squares , causal model , computer science , artificial intelligence , mathematical analysis
Summary A framework for causal inference from two‐level factorial designs is proposed, which uses potential outcomes to define causal effects. The paper explores the effect of non‐additivity of unit level treatment effects on Neyman's repeated sampling approach for estimation of causal effects and on Fisher's randomization tests on sharp null hypotheses in these designs. 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.