TABLE 3.

A Procedure for Declaring and Diagnosing Research Designs Using the Companion Software DeclareDesign (Blair et al. 2018)

	Design declaration	Code
$\mathrm{M}\{\begin{array}{l}\mathrm{Declare}\mathrm{background}\mathrm{variables}\\ \mathrm{Declare}\mathrm{functional}\mathrm{relations}\end{array}$		p_U <-declare_population (N = 200, u = rnorm(N))
		f_Y <- declare potential outcomes(Y ~Z + u)
I	Declare inquiry	I <- declare_estimand (ATE=mean(Y_Z_1 − Y_Z_0))
$\mathrm{D}\{\begin{array}{l}\mathrm{Declare}\mathrm{sampling}\\ \mathrm{Declare}\mathrm{assignment}\\ \mathrm{Declare}\mathrm{outcome}\mathrm{revelation}\end{array}$		p_S <-declare_sampling (n = 100) p_Z <- declare_assignment (m = 50)
		R <- declare_reveal (Y, Z)
A	Declare answer strategy	A <- declare estimator (Y ~Z, estimand = “ATE”)
	Declare design, (M, I, D, A)	design <- p_U + f_Y + I + p_S + p_Z + R + A
	Design simulation (1 draw)	Code
1	Draw a population u using P(U)	u <- p_U()
2	Generate potential outcomes using fY	D <- f_Y(u)
3	Calculate estimand aM	a_M <- I(D)
4	Draw data, d, given model assumptions and data strategies	d <- R(p_Z(p_S(D)))
5	Calculate answers, aA using A and d	a_A <- A(d)
6	Calculate a diagnostic statistic t using aA and aM	e <- a_A[“estimate”] — a_M[“estimand”]
Design diagnosis (m draws)		Code
Declare a diagnosand		bias <- declare_diagnosands(bias = mean(estimate − estimand))
Calculate a diagnosand		diagnose_design(design_diagnosands = bias, sims = m)

Note: The top panel includes each element of a design that can be declared along with code used to declare them. The middle panel describes steps to simulate that design. The bottom panel includes the procedure to diagnose the design.