Table 1.

Data example of a replicate/scenario, estimated effects (coefficients from logistic models) of exposure (X ₁), and known “causal”/confounding factors of A on A and proxy outcome C.

	Effects on A		Effects on C		Indicators for real causal factors   (1 = Yes, 0 = No)
	Coefficient	P value	Coefficient	P value	Causal to A	Causal to C
X 1	0.79	0.000	0.09	0.011	1	0
X 2	0.19	0.000	0.92	0.000	0	1
X 3	0.69	0.000	0.03	0.360	1	0
X 4	0.46	0.000	0.50	0.000	0	0
X 5	∗		∗		1	1
X 6	∗		∗		1	1
X 7	0.87	0.000	0.29	0.000	1	0
X 8	0.20	0.000	0.84	0.000	0	1
X 9	0.04	0.293	0.80	0.000	0	1
X 10	0.77	0.000	0.13	0.000	1	0
X 11	∗		∗		1	0
X 12	0.15	0.000	0.79	0.000	0	1
X 13	∗		∗		1	0
X 14	∗		∗		0	0
X 15	0.85	0.000	0.91	0.000	1	1
X 16	∗		∗		1	1
X 17	0.73	0.000	0.10	0.009	1	0
X 18	0.15	0.000	0.89	0.000	0	1
X 19	∗		∗		0	1
X 20	0.95	0.000	0.35	0.000	1	0
X 21	∗		∗		0	0
X 22	0.27	0.000	0.16	0.000	0	0
X 23	0.25	0.000	0.19	0.000	0	0
X 24	0.20	0.000	0.18	0.000	0	0
X 25	∗		∗		0	0
X 26	∗		∗		0	0
X 27	∗		∗		0	0
X 28	∗		∗		0	0
X 29	0.50	0.000	0.47	0.000	0	0
X 30	∗		∗		0	0
X 31	∗		∗		0	0
X 32	∗		∗		0	0
X 33	∗		∗		0	0
X 34	∗		∗		0	0
X 35	∗		∗		0	0
X 36	∗		∗		0	0
X 37	∗		∗		0	0
X 38	0.13	0.001	0.17	0.000	0	0
X 39	∗		∗		0	0
X 40	0.16	0.000	0.17	0.000	0	0

*indicates variable is not known as a “causal” factor for A, therefore is not included in the models.