Let's say that a data set has N random binary variables Xi and we want to infer which of these variables have a causal relationship with X1. The following table would describe the data, where each column is an observable moment in time... The dataset is very large and the number of observations can be as big as needed
X1 1 1 1 0 0 0 0
X2 0 1 0 1 1 0 1
X3 1 0 1 1 1 0 0
...
Xn 1 1 1 1 0 1 0
There is a population of M individuals, each of these individuals has a different table, but we can use these M individuals to develop the required statistics to infer causality (not correlation)
How can this be done?