The Theory of the Design of Experiments

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Overlapping experiment infrastructure: more, better, faster experimentation (2010, methodology): “Multi-factorial experiments are backed by copious theory and practice [ 3 ].”
Rerandomization and regression adjustment (2019, result): “Freedman’s (2008a, b) analyses allowed for treatment effect heterogeneity, in contrast with the existing literature on ANCOVA which often assumed additive treatment effects (Fisher, 1935; Kempthorne, 1952; Cox and Reid, 2000).”
Why Experimenters Might Not Always Want to Randomize, and What They Could Do Instead (2016, background): “There is a large and old literature on experimental design in statistics, going back at least to Smith (1918), and receiving broader attention since Kiefer and Wolfowitz (1959) and related contributions.”
Stratification Trees for Adaptive Randomization in Randomized Controlled Trials (2018, background): “The literature on randomization in RCTs is vast (references in Athey and Imbens, 2017; Cox and Reid, 2000; Glennerster and Takavarasha, 2013; Pukelsheim, 2006; Rosenberger and Lachin, 2015, provide an overview).”
Robust, accurate confidence intervals with a weak instrument: quarter of birth and education (2005, background): “Pitman (1937), Welch (1937), Kempthorne (1955), Wilk (1955), Cox (1958), Robinson (1973), Tukey (1985), Gail et al. (1988) and Cox and Reid (2000) ), a quantity whose value is determined by the random choice of P from Ω is a random variable because P is random.”