In causal inference, the basic postulate that "the factorization of the joint density function $P_{cause,effect}$ into $P_{cause} P_{effect|cause}$ should lead to a simpler model than $P_{effect} P_{cause|effect}$" can be expressed using the Kolmogorov complexity framework: $K(P_{cause}) + K(P_{effect|cause}) \overset{+}\leq K(P_{effect}) + K(P_{cause|effect})$ This inequality derives from the postulate of algorithmic independence between the distribution of the cause $P_{cause}$ and the distribution of the causal mechanism $P_{effect|cause}$, stated by Janzing and Schölkopf as: $I(P_{cause} : P_{effect|cause}) \overset{+}=0$ where $I(P_{cause} : P_{effect|cause})$ denotes algorithmic mutual information. Since Kolmogorov complexity and algorithmic mutual information are not computable in practice, they have inspired practical implementations such as model selection with the Minimum Message Length (MML) principle and methods exploiting the independence between cause and mechanism.