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Finding the right causal tool for the right complex job

In navigating complex systems, Dr. Matthew Berryman emphasizes the importance of selecting appropriate causal tools. He presents the Total Systems Intervention (TSI) framework, which encompasses methodologies like system dynamics and soft systems methodology, guiding users through creativity, choice, and implementation phases. Drawing on knowledge-based expert systems, he illustrates forward and backward chaining techniques for method selection. While discussing the strengths and limitations of various causal methods including Granger causality, Bayesian belief networks, and Markov random networks, he advocates for adaptability and the refinement of decision-making processes in causal analysis.

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Finding the right causal tool for the right complex job

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  1. Finding the right causal tool for the right complex job Dr Matthew Berryman

  2. Inspiration TSI • Total systems intervention (Flood & Jackson): • An umbrella framework for guiding the choice of systems methodologies (system dynamics, soft systems methodology, etc.) • 3 phases: creativity, choice, implementation.

  3. Proposed structure • Knowledge-based expert system: • Set of if-then rules. • Easy for humans to read & follow • Natural to break on distinguishing features.

  4. Chaining • Forward chaining: • Start with the data available – details of the problem, and system – and work forwards to reach a conclusion – decision as to which method(s) to use. • Backwards chaining: • Start with a method, and work out what the problem & system would look like. • If the expert system can’t identify a method, then pick the one that’s closest and work back.

  5. Problems • Only as good as the expert(s). • In terms of rules for distinguishing between the different methods. • In terms of what methods are considered as outcomes. • May be more than one for a completely specified set of data. • Only as good as the user(s). • Has the user correctly identified all the distinguishing characteristics? • There may be multiple reasonable views of the system and hence multiple correct sets of distinguishing characteristics. • Does the user follow it blindly (deliberately, or unknowingly)?

  6. Granger causality • Based on whether the past can give an improved forecast of the future (causality can only go forwards). • Stronger than just using correlation (avoids the sea level in Venice / bread price in the UK problem), but not 100% evidence for causality. • Different statistical tests can be used: • Original (regression based on asymptotic distribution theory) – can’t handle non-stationarity. • Vector Error Correction Model (VECM). • Vector AutoRegressive (VAR) model. • Toda-Yamamoto modified Wald test.

  7. Bayesian belief networks • Problems: • Represent subjective beliefs. Assume fixed set of variables, and compute the probabilities. Can update the probabilities, but not the structure. • Can’t have cycles (A→B→C→A). Image from: http://cli.vu/pubdirectory/67/huygen50.png

  8. Markov random networks • Benefits: • Can handle cycles. • Better training than BBNs. • Problems: • Can’t specify whether it’s A→B or B→A. Image from: http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV0809/ORCHARD/

  9. Causal State Splitting Reconstruction • Doesn’t presuppose a causal structure, instead it infers one (the maximally predictive, minimal space) one from the data. • Disadvantage: • Applies to an output of a discretised (time and value dimensions) 1D time series data (x[k], x and k discrete). • Some extensions of ideas to 2D CAs but they rely on the specific nature of CAs in constructing causal states.

  10. Combing this • if (you want to find causal relationships) { If (1D time series) { CSSR } else { Granger } } else (if you want to analyse a causal system with known relationships) { if (cycles) { Markov } else { BBN } }

  11. Adaptive • Adapt the decision tree. • Fine tune the existing (exploitation, level 1) causal methods. • Develop new ones (level 2). • Proxies.

  12. Conclusions • Despite limitations, I believe this to be a useful way of organising the set of causal methods we will research. • High-level descriptions. • Be adaptive!

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