library(tidyverse)

tb <- tibble(A_effect = sort(rep(seq(-.5,.5,.002),501)),
             B_effect = rep(seq(-.5,.5,.002),501)) %>%
  mutate(noA_noB = .5,
         A_noB = .5 + A_effect,
         noA_B = .5 + B_effect,
         A_B = .5 + A_effect + B_effect) %>%
  mutate(B_among_A = (A_B)/(A_B + A_noB),
         B_among_nonA = (noA_B)/(noA_B+noA_noB)) %>%
  mutate(effect = ifelse(A_B < 0 | A_B > 1, NA, B_among_A - B_among_nonA))

ggplot(tb, aes(x = A_effect, y = B_effect)) +
  geom_raster(aes(fill = effect), interpolate=TRUE) +
  scale_fill_gradient2(low="red", mid="blue", high="red",
                       midpoint=0, limits=range(tb$effect),
                       name = "Collider Bias") +
  theme_classic() +
  labs(x = "Effect of binary A on P(C = 1)",
       y = "Effect of binary B on P(C = 1)",
       title = "Estimated linear effect of A on B when selecting on collider",
       caption = "A, B, C are binary w/ mean .5. True A and B relationship is 0.\nGray is when combined A and B effect gives P(C) outside 0 and 1.")