Full example

This section is here to help us solidify our newly acquired knowledge. We’ll run a full causal inference process once again, step by step. We’ll introduce some new exciting elements on the way and – finally – we’ll translate the whole process to the new GCM API. By the end of this section, you will have the confidence and skills to apply the four-step causal inference process to your own problems.

Figure 7.4 presents a graphical model that we’ll use in this section:

Figure 7.4 – A graphical model that we’ll use in this section

We’ll generate 1,000 observations from an SCM following the structure from Figure 7.4 and store them in a data frame:

SAMPLE_SIZE = 1000
S = np.random.random(SAMPLE_SIZE)
Q = 0.2*S + 0.67*np.random.random(SAMPLE_SIZE)
X = 0.14*Q + 0.4*np.random.random(SAMPLE_SIZE)
Y = 0.7*X + 0.11*Q + 0.32*S +
    0.24*np.random.random(SAMPLE_SIZE...