While conventional approaches to causal inference are mainly based on conditional (in)dependences, recent methods also account for the shape of (conditional) distributions. The idea is that the causal hypothesis “X causes Y” imposes that the marginal distribution PX and the conditional distribution PY|X represent independent mechanisms of nature. Recently it has been postulated that the shortest description of the joint distribution PX,Y should therefore be given by separate descriptions of PX and PY|X. Since description length in the sense of Kolmogorov complexity is uncomputable, practical implementations rely on other notions of independence. Here we define independence via orthogonality in information space. This way, we can explicitly describe the kind of dependence that occurs between PY and PX|Y making the causal hypothesis “Y causes X” implausible. Remarkably, this asymmetry between cause and effect becomes particularly simple if X and Y are deterministically related. We present an inference method that works in this case. We also discuss some theoretical results for the non-deterministic case although it is not clear how to employ them for a more general inference method
This survey/interview is a part of a research project titled "Complex research of augmented reality for the blind and weak-sighted people" (project No. 01.2.2-LMT-K-718-01-0060) funded by European Regional Development. The overall goal of the project is development of a functional computer vision-based travelling aid for the blind and weak-sighted people. This survey aims to identify and describe requirements and expectations visually impaired users have for such technological solutions. The survey is strictly anonymous. The project team is grateful for your time and effort dedicated for filling in this questionnaire/participating in this interview.
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