1 research outputs found
Telling Cause from Effect using MDL-based Local and Global Regression
We consider the fundamental problem of inferring the causal direction between
two univariate numeric random variables and from observational data.
The two-variable case is especially difficult to solve since it is not possible
to use standard conditional independence tests between the variables.
To tackle this problem, we follow an information theoretic approach based on
Kolmogorov complexity and use the Minimum Description Length (MDL) principle to
provide a practical solution. In particular, we propose a compression scheme to
encode local and global functional relations using MDL-based regression. We
infer causes in case it is shorter to describe as a function of
than the inverse direction. In addition, we introduce Slope, an efficient
linear-time algorithm that through thorough empirical evaluation on both
synthetic and real world data we show outperforms the state of the art by a
wide margin.Comment: 10 pages, To appear in ICDM1