Causal inference from 2K factorial designs by using potential outcomes

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General Forms of Finite Population Central Limit Theorems with Applications to Causal Inference (2016, methodology): “Following the notation by Dasgupta, Pillai, and Rubin (2015), each factorial effect can be characterized by a column vector with half of its elements being +1 and the other half being −1.”
Causal Interaction in Factorial Experiments: Application to Conjoint Analysis (2018, background): “…following difference-inmeans estimators have been used to nonparametrically estimate the ACE and AME (e.g., Hainmueller, Hopkins, and Yamamoto 2014; Dasgupta, Pillai, and Rubin 2015): τ̂ j j′ ( ,m; 0, 0) = ∑n i=1Yi1{Ti j = ,Ti j′ = m}∑n i=1 1{Ti j = ,Ti j′ = m} − ∑n i=1Yi1{Ti j = 0,Ti j′ = 0}∑n…”
Covariate-adjusted Fisher randomization tests for the average treatment effect (2020, unknown): “nalogs based on large-sample approximations. We conjecture that the strategy of appropriately studentizing an ecient, covariate-adjusted estimator works for frt in general experiments as well (e.g., Dasgupta et al. 2015; Lu 2016; Mukerjee et al. 2018; Middleton 2018; Fogarty 2018a,b). This strategy ”