Conducting Causal Analysis by Means of Approximating Probabilistic Truths.

ABSTRACT: The current paper develops a probabilistic theory of causation using measure-theoretical concepts and suggests practical routines for conducting causal inference. The theory is applicable to both linear and high-dimensional nonlinear models. An example is provided using random forest regressions and daily data on yield spreads. The application tests how uncertainty in short- and long-term inflation expectations interacts with spreads in the daily Bitcoin price. The results are contrasted with those obtained by standard linear Granger causality tests. It is shown that the suggested measure-theoretic approaches do not only lead to better predictive models, but also to more plausible parsimonious descriptions of possible causal flows. The paper concludes that researchers interested in causal

SUBMITTER: Andree BPJ 

PROVIDER: S-EPMC8774820 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature