Bayes ball

bayesball.jl

using CausalInference
V = [:U, :T, :P, :O]
g = digraph([1=>3, 2=>3, 3=>4, 2=>4, 1=>4])

# Can estimate total effect T=>O without observing U?
u = 2
v = 4
∅ = Set{Int}()
observed = 2:4
collect(list_covariate_adjustment(g, u, v, ∅, observed))
# No adjustment needed.

# What happens if we condition on P?
u = 2
C = [3]
V2 = [:U, :U, :T, :T, :P, :P, :O, :O] # bayesball_graph has each vertex twice
g2 = CausalInference.bayesball_graph(g, u, C)

using GraphMakie, GLMakie, NetworkLayout
fig = Figure(resolution=(500, 500))
ax1 = Axis(fig[1,1])
hidespines!(ax1)
hidedecorations!(ax1)
graphplot!(ax1, g; layout=Stress(), arrow_shift = :end, ilabels=V, arrow_size=25, ilabels_fontsize = 30)
fig


ax2 = Axis(fig[1,2])
hidespines!(ax2)
hidedecorations!(ax2)
graphplot!(ax2, g2; ilabels=V2, arrow_size=25, ilabels_fontsize = 30)
fig