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… Show more
“…Paper [ 15 ] develops a probabilistic theory of causation using measure-theoretical concepts and information-theoretic functionals and suggests practical routines for conducting causal inference. The theory is applicable to both linear and high-dimensional nonlinear models.…”
The present Special Issue of Entropy, entitled "Causal Inference for Heterogeneous Data and Information Theory", covers various aspects of causal inference [...]
“…Paper [ 15 ] develops a probabilistic theory of causation using measure-theoretical concepts and information-theoretic functionals and suggests practical routines for conducting causal inference. The theory is applicable to both linear and high-dimensional nonlinear models.…”
The present Special Issue of Entropy, entitled "Causal Inference for Heterogeneous Data and Information Theory", covers various aspects of causal inference [...]
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