Randomization-Based Causal Inference from Unbalanced 2^2 Split-Plot Designs

Anqi Zhao; Peng Ding; Tirthankar Dasgupta

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Randomization-Based Causal Inference from Unbalanced 2^2 Split-Plot Designs

Anqi Zhao, Peng Ding, Tirthankar Dasgupta

Methodology · Submitted 11 Feb 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Superiority over existing model-based alternatives is reported under a variety of settings for both binary and continuous outcomes.

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arXiv ID1602.03915 (version 1)

Pith ID3FUSNRJI, Pith's permanent record of this paper and its review

CitationAnqi Zhao, Peng Ding, Tirthankar Dasgupta. "Randomization-Based Causal Inference from Unbalanced 2^2 Split-Plot Designs." arXiv:1602.03915 (2016). https://pith.science/paper/3FUSNRJI

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What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.23 · human participants 0.12 · controlled comparison 0.10 · randomized assignment 0.74 · states quantitative result 0.11 · compares with prior results 0.97 · mentions code or data availability 0.02.

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arXiv listing abstract v1. Results and the main claim are underlined; show every role.

Given two 2-level factors of interest, a 2^2 split-plot design} (a) takes each of the $2^2=4$ possible factorial combinations as a treatment, (b) identifies one factor as `whole-plot,' (c) divides the experimental units into blocks, and (d) assigns the treatments in such a way that all units within the same block receive the same level of the whole-plot factor. Assuming the potential outcomes framework, we propose in this paper a randomization-based estimation procedure for causal inference from 2^2 designs that are not necessarily balanced. Sampling variances of the point estimates are derived in closed form as linear combinations of the between- and within-block covariances of the potential outcomes. Results are compared to those under complete randomization as measures of design efficiency. Interval estimates are constructed based on conservative estimates of the sampling variances, and the frequency coverage properties evaluated via simulation. Asymptotic connections of the proposed approach to the model-based super-population inference are also established. Superiority over existing model-based alternatives is reported under a variety of settings for both binary and continuous outcomes.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Given two 2-level factors of interest, a 2^2 split-plot design} (a) takes each of the $2^2=4$ possible factorial combinations as a treatment, (b) identifies one factor as `whole-plot,' (c) divides the experimental units into blocks, and (d) assigns the treatments in such a way that all units within the same block receive the same level of the whole-plot factor.	background 1.00	0.06	0.02	asserted 0.99
s1	Assuming the potential outcomes framework, we propose in this paper a randomization-based estimation procedure for causal inference from 2^2 designs that are not necessarily balanced.	contribution 0.99	0.93	0.10	asserted 0.98
s2	Sampling variances of the point estimates are derived in closed form as linear combinations of the between- and within-block covariances of the potential outcomes.	method 0.51	0.94	0.06	asserted 1.00
s3	Results are compared to those under complete randomization as measures of design efficiency.	method 0.76	0.83	0.05	asserted 0.87
s4	Interval estimates are constructed based on conservative estimates of the sampling variances, and the frequency coverage properties evaluated via simulation.	method 0.95	0.94	0.04	asserted 0.97
s5	Asymptotic connections of the proposed approach to the model-based super-population inference are also established.	contribution 0.61	0.84	0.04	asserted 1.00
s6	Superiority over existing model-based alternatives is reported under a variety of settings for both binary and continuous outcomes.	result 0.96	0.88	0.68	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from proof or derivation 0.54.

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Original abstract

Given two 2-level factors of interest, a 2^2 split-plot design} (a) takes each of the $2^2=4$ possible factorial combinations as a treatment, (b) identifies one factor as `whole-plot,' (c) divides the experimental units into blocks, and (d) assigns the treatments in such a way that all units within the same block receive the same level of the whole-plot factor. Assuming the potential outcomes framework, we propose in this paper a randomization-based estimation procedure for causal inference from 2^2 designs that are not necessarily balanced. Sampling variances of the point estimates are derived in closed form as linear combinations of the between- and within-block covariances of the potential outcomes. Results are compared to those under complete randomization as measures of design efficiency. Interval estimates are constructed based on conservative estimates of the sampling variances, and the frequency coverage properties evaluated via simulation. Asymptotic connections of the proposed approach to the model-based super-population inference are also established. Superiority over existing model-based alternatives is reported under a variety of settings for both binary and continuous outcomes.

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