Causal inference from 2K factorial designs by using potential outcomes

Tirthankar Dasgupta, Natesh S. Pillai, Donald B. Rubin

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)727-753
Number of pages27
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Volume77
Issue number4
DOIs
StatePublished - Sep 1 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Experimental design
  • Fisherian inference
  • Neymanian inference
  • Rubin's causal model

Fingerprint Dive into the research topics of 'Causal inference from 2<sup>K</sup> factorial designs by using potential outcomes'. Together they form a unique fingerprint.

Cite this