A framework for causal discovery in non-intervenable systems.

  title={A framework for causal discovery in non-intervenable systems.},
  author={Peter Jan van Leeuwen and Michael DeCaria and Nachiketa Chakraborty and Manuel Pulido},
  volume={31 12},
Many frameworks exist to infer cause and effect relations in complex nonlinear systems, but a complete theory is lacking. A new framework is presented that is fully nonlinear, provides a complete information theoretic disentanglement of causal processes, allows for nonlinear interactions between causes, identifies the causal strength of missing or unknown processes, and can analyze systems that cannot be represented on directed acyclic graphs. The basic building blocks are information theoretic… 


D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery
This work primarily focuses on modern, continuous optimization methods, and provides reference to further resources such as benchmark datasets and software packages.
Granger Causality: A Review and Recent Advances
  • A. Shojaie, E. Fox
  • Computer Science
    Annual Review of Statistics and Its Application
  • 2021
Recent advances that address various shortcomings of the earlier approaches are discussed, from models for high-dimensional time series to more recent developments that account for nonlinear and non-Gaussian observations and allow for subsampled and mixed-frequency time series.
Theory I: Prediction
A Survey of Learning Causality with Data
This survey provides a comprehensive and structured review of both traditional and frontier methods in learning causal effects and relations along with the connections between causality and machine learning.
Partial cross mapping eliminates indirect causal influences
A data-driven model-independent method to distinguish direct from indirect causality and test its applicability to real-world data, which is expected to be indispensable in unlocking and deciphering the inner mechanisms of real systems in diverse disciplines from data.
Bayesian Symbolic Regression
The proposed BSR(Bayesian Symbolic Regression) method saves computer memory with no need to keep an updated 'genome pool', and numerical experiments show that, compared with GP, the solutions of BSR are closer to the ground truth and the expressions are more concise.
Inferring causation from time series in Earth system sciences
An overview of causal inference frameworks is given, promising applications and methodological challenges are identified, and a causality benchmark platform is initiated to close the gap between method users and developers.
Review of Causal Discovery Methods Based on Graphical Models
This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three decades, including constraint-based and score-based methods and those based on functional causal models, supplemented by some illustrations and applications.
Causal network reconstruction from time series: From theoretical assumptions to practical estimation.
The problem of inferring causal networks including time lags from multivariate time series is recapitulated from the underlying causal assumptions to practical estimation problems and method performance evaluation approaches and criteria are suggested.
Faithfulness, Coordination and Causal Coincidences
Within the causal modeling literature, debates about the Causal Faithfulness Condition (CFC) have concerned whether it is probable that the parameters in causal models will have values such that