Preprints
https://doi.org/10.5194/gmd-2022-106
https://doi.org/10.5194/gmd-2022-106
Submitted as: methods for assessment of models
25 Apr 2022
Submitted as: methods for assessment of models | 25 Apr 2022
Status: this preprint was under review for the journal GMD but the revision was not accepted.

Empirical Assessment of Normalized Information Flow for Quantifying Causal Contributions

Chin-Hsien Cheng1,2 and Simon Redfern1 Chin-Hsien Cheng and Simon Redfern
  • 1Asian School of the Environment, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
  • 2Joint International Research Laboratory of Climate and Environment Change, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China

Abstract. To understand the plethora of important processes that are characterized by their complexity, such as global climate change, it is important to quantify causal contributions between time series variables. Here, we examine the hypothesis that the normalized causal sensitivity (nCS) can be measured by the (modified) normalized information flow, nIF (or mdnIF). The instantaneous causal sensitivity is defined by absolute causal contributions to the effect variable over the change in cause variable. The nCS needs to be comparable among i) causes, ii) at different times and iii) from various locations. Therefore, if our hypothesis holds, the nIF must also fulfil these three requirements. We verify, empirically, that the causal contributions between variables can be reasonably estimated by the product of a constant “maximal causal sensitivity” and a modified nIF. Between opposite causal directions, causal sensitivity can be further normalized by the larger “maximal causal sensitivity”. Our method is useful when there are: i) strong but hard-to-quantify noise contributions to the effect variable, ii) significant causal time-lags with a need to estimate the lag, iii) many causes from various locations to an overall mean effect with a need to differentiate their causal contributions, or iv) causal contributions at higher order.

Chin-Hsien Cheng and Simon Redfern

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-106', Anonymous Referee #1, 21 May 2022
    • AC2: 'Reply on RC1', Simon Redfern, 13 Jul 2022
  • RC2: 'Comment on gmd-2022-106', Anonymous Referee #2, 06 Jun 2022
    • AC3: 'Reply on RC2', Simon Redfern, 13 Jul 2022
  • AC1: 'Comment on gmd-2022-106', Simon Redfern, 13 Jul 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-106', Anonymous Referee #1, 21 May 2022
    • AC2: 'Reply on RC1', Simon Redfern, 13 Jul 2022
  • RC2: 'Comment on gmd-2022-106', Anonymous Referee #2, 06 Jun 2022
    • AC3: 'Reply on RC2', Simon Redfern, 13 Jul 2022
  • AC1: 'Comment on gmd-2022-106', Simon Redfern, 13 Jul 2022

Chin-Hsien Cheng and Simon Redfern

Chin-Hsien Cheng and Simon Redfern

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Short summary
Causality is one of the foundations of scientific understanding and progress. Statistical models extrapolate historical trends into the future through statistical tools, but may still lack insight into the physical underlying processes. We have developed a method to quantify physical causal contributions between observational time series. It plugs the gap between process-based and statistical models, providing a key to unlocking and understanding causality in Earth systems science processes.