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Table 2 The bias and RMSE for \(\tau _{\textrm{SATE}}\) under quasi-rerandomization (QReR) and rerandomization (ReR) under different combinations of the acceptance probability \(p_a\), covariate scenarios and response surfaces when \(r=N_0/N_1=1\). The average Monte Carlo standard errors (MCSEs) of bias are 0.03, 0.04 and 0.31 and those of RMSE are 0.02, 0.03 and 0.31 for Linear, NonLinear (Interaction) and NonLinear (Polynomial) models, respectively

From: Quasi-rerandomization for observational studies

Response

\(p_a\)

Method

Scenario 1

Scenario 2

Scenario 3

   

Bias

RMSE

Bias

RMSE

Bias

RMSE

Linear

0.1

\(\textrm{QReR}_{\textrm{S}}\)

-0.002

0.35

0.005

0.44

0.020

0.42

  

\(\textrm{QReR}_{\textrm{M}}\)

-0.022

0.12

-0.032

0.12

-0.028

0.12

  

ReR

0.004

0.31

0.001

0.39

0.003

0.41

 

0.5

\(\textrm{QReR}_{\textrm{S}}\)

-0.053

0.47

-0.059

0.60

-0.067

0.66

  

\(\textrm{QReR}_{\textrm{M}}\)

-0.037

0.12

-0.059

0.13

-0.058

0.13

  

ReR

0.029

0.45

0.038

0.53

-0.001

0.56

 

1

\(\textrm{QReR}_{\textrm{S}}\)

-0.035

0.60

-0.017

0.67

-0.062

0.76

  

\(\textrm{QReR}_{\textrm{M}}\)

-0.054

0.12

-0.064

0.14

-0.060

0.14

  

ReR

-0.023

0.57

-0.038

0.68

0.072

0.72

NonLinear

0.1

\(\textrm{QReR}_{\textrm{S}}\)

-0.017

0.49

-0.060

0.64

-0.071

0.62

(Interaction)

 

\(\textrm{QReR}_{\textrm{M}}\)

-0.033

0.19

-0.123

0.24

-0.132

0.25

  

ReR

0.014

0.46

0.011

0.54

0.032

0.62

 

0.5

\(\textrm{QReR}_{\textrm{S}}\)

-0.079

0.65

-0.162

0.83

-0.199

0.93

  

\(\textrm{QReR}_{\textrm{M}}\)

-0.055

0.19

-0.148

0.25

-0.165

0.27

  

ReR

0.041

0.63

0.053

0.74

-0.020

0.76

 

1

\(\textrm{QReR}_{\textrm{S}}\)

-0.061

0.84

-0.095

0.94

-0.166

1.10

  

\(\textrm{QReR}_{\textrm{M}}\)

-0.075

0.20

-0.148

0.25

-0.152

0.27

  

ReR

-0.037

0.80

-0.058

0.95

0.090

1.02

NonLinear

0.1

\(\textrm{QReR}_{\textrm{S}}\)

0.078

2.31

5.001

7.46

-5.931

7.69

(Polynomial)

 

\(\textrm{QReR}_{\textrm{M}}\)

0.025

2.04

4.869

7.02

-6.118

7.58

  

ReR

0.030

2.03

-0.571

5.75

0.368

5.77

 

0.5

\(\textrm{QReR}_{\textrm{S}}\)

-0.051

2.54

4.958

7.75

-6.869

8.77

  

\(\textrm{QReR}_{\textrm{M}}\)

-0.063

2.12

4.861

7.23

-6.551

7.98

  

ReR

-0.007

1.94

-0.289

5.60

0.119

6.16

 

1

\(\textrm{QReR}_{\textrm{S}}\)

-0.051

2.65

4.774

7.54

-7.008

8.77

  

\(\textrm{QReR}_{\textrm{M}}\)

-0.071

2.15

4.703

7.05

-7.229

8.61

  

ReR

-0.048

2.07

-0.029

6.03

-0.020

6.48

  1. \(\textrm{QReR}_{\textrm{S}}\): using a single random weight vector generated by our model to conduct inference; \(\textrm{QReR}_{\textrm{M}}\): using the average weight vector based on \(M=1000\) random weights