Causal Inference for Heterogeneous Data and Information Theory

Dear Colleagues,

Detecting causal effect from observational data has attracted a lot of attention in the past decades. Much of the collected observational data however does not come from the traditional statistical setting of randomized experiments. The data collected from different sources is often heterogeneous due to changing circumstances, unobserved confounders, time-shifts in the distribution or due to the various time scales or definition domains of measures observations. These heterogeneities make it difficult to gain valid conclusions about causal effects that generalize well to new data.

This Special Issue focuses on causal inference models for heterogeneous data of (not only) the described heterogeneous nature. As working approaches and tools to be selected here is information theory, probability and to them related machine learning tools. Information theory here is to be interpreted broadly, including, for instance, classical algorithmic information theory, compression schemes, stochastic complexity, statistical information theory as well as control theory. The Special Issue is open to papers that are, in their essence of fundamental theoretical research, although demonstration on real or synthetic datasets is encouraged when possible.

This Special Issue will gather the current approaches to the following (and related) topics:

• Linear and non-linear structural equation models
• Causal models for categorical and integer-valued variables
• Graphical models
• Probabilistic interactive networks
• Latent variables, confounders
• Additive noise models
• Causal inference by information-theoretic criteria
• Kolmogorov complexity and causality
• Transfer entropy

Dr. Kateřina Hlaváčková-Schindler
Guest Editor

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