Causal approximations are based on the idea that more approximate descriptions usually explain less about a phenomenon than more accurate descriptions.

Feb 1, 2023 · In this paper we introduce a new class of approximations, called cuusal approximations, that are commonly found in modeling the physical world.

In particular, we introduce causal approximations, a new class of approximations that form the basis of an efficient model selection algorithm. The basic idea.

Aug 26, 2018 · The simplest causal system is one in which features determine actions and the combination of features and actions determine the outcome.

Causal approximations support the efficient generation of parsimonious causal explanations. Parsimonious causal explanations play an important role in reasoning ...

Adequate problem representations require the identification of abstractions and approximations that are well suited to the task at hand.

The present chapter considers the causal approximations of causal transfer ... (B) Causality: The approximation 2N f should represent a causal transfer.

Aug 26, 2018 · In general I think you want to avoid regularization in causal inference because what you are doing is regularizing away your causal effect! This ...

This paper studies the approximation of continuous functions in sub- sets of all causal and stable transfer functions. Such approximations play a central roll ...

This paper studies the approximation of continuous functions in subsets of all causal and stable transfer functions. Such approximations play a central roll ...