finding the right causal tool for the right complex job

Dr Matthew Berryman

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.

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

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.

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)?

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.

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

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/

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.

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

}}

Adapt the decision tree. Fine tune the existing (exploitation, level 1)

causal methods. Develop new ones (level 2). Proxies.

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!