Unit A2.1 Causality

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Unit A2.1 Causality

• Kenneth D. Forbus
• Qualitative Reasoning Group
• Northwestern University

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Overview

• What is causality?
• Design choices for causality in qualitative
  physics
• Using causality
• Example Self-explanatory simulators

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A qualitative physics view of causation

• There are several broadly used notions of
  causality in reasoning about the physical world
• They can be decomposed by several factors,
  including
• Ontological assumptions Is there a class of
  entities that act as mechanisms in the domain?
• Measurement scenario What sense of change is
  being discussed?

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Measurement Scenarios affect causality

• Incremental
• Cause precedes effect

Continuous Cause, effect coextensive
Heat flow causes heat of water to rise, which
causes temperature of water to rise
Moving soup spoon causes the napkin to wipe your
face
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Implications for theories of causal reasoning

• Consider the following
• Causes must precede effects in mechanistic
  situations, but causes are temporally coextensive
  in continuous causation.
• Ontological assumptions used by human experts
  vary with domain
• cf. use of processes versus components in
  thermodynamics versus electronics
• ? No single, simple account of causality is
• sufficient.
• ? Gold standard is psychology, not physics

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Causality via Propagation

• Source of causation is a perturbation or input
  (de Kleer Brown, 1984)
• Changes propagate through constraint laws
• Useful in domains where number of physical
  process instances is very large

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Mythical Causality

• What a system does between quasistatic states
• Extremely short period of time within which
  incremental causality operates, even in
  continuous systems
• Motivation Capture intuitive explanations of
  experts about causality in continuous systems,
  without violating philosophical ideas such as A
  Cause must precede its effect

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Implications of causality as propagation

• Identifies order of causality with order of
  computation.
• No input ? no causality
• Quantitative analog Simulators like SPICE
  require an order of computation to drive them.

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Causality in QP theory(Forbus, 1981 1984)

• Sole Mechanism assumption All causal changes
  stem from physical processes
• Changes propagate from quantities directly
  influenced by processes through causal laws to
  indirectly influenced quantities
• Naturally models human reasoning in many domains
  (i.e., fluids, heat, motion)

I-
I
Liquid FlowF ? G
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Implications of Sole Mechanism assumption

• All natural changes must be traced back to the
  action of some physical process
• If not so explained, either an agent is involved,
  or a closed-world assumption is incorrect
• The scenario isnt fully or accurately known
• The reasoners process vocabulary is incomplete
  or incorrect
• Syntactic enforcement Direct influences only
  appear in descriptions of physical processes
• Causal direction in qualitative relations crucial
  for ensuring correct causal explanations

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How directional are causal laws?

• Answer It depends
• In some domains, clear causal direction across
  broad variety of situations
• cf. engineering thermodynamics
• In some domains, causal direction varies across
  broad variety of situations
• cf. analog electronics

T f(heat, mass, )
V I R
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Causal Ordering

• Used by H. Simon in economics in 1953
• Inputs
• Set of equations (quantitative or qualitative)
• Subset of parameters identified as exogenous
• Output
• Directed graph of causal relationships

• Method (informal)
• Exogenous parameters comprise starting set of
  explained parameters
• Find all equations that have exactly one
  parameter not yet explained.
• Add causal links from explained parameters to the
  unexplained parameter
• Add unexplained parameter to set of explained
  parameters
• Continue until exhausted

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Tradeoffs in causal ordering algorithm

• Advantages
• Can provide causal story for any set of equations
  
• Assuming well-formed and enough exogenous
  parameters
• Causal story can change dynamically if what is
  exogenous changes

• Drawbacks
• Poor choice of exogenous parameters can lead to
  psychologically implausible causal stories
• e.g., the increase in blood sodium goes up,
  which causes the blood volume to go up.
• Does not specify the sign of causal effect

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Self-Explanatory Simulators

• Idea Integrate qualitative and numerical
  representations to achieve
• Precision and speed of numerical simulation
• Explanatory power of qualitative physics
• Imagine
• SimEarth with explanations
• Interactive, active illustrations in textbooks
• Training simulators with debriefing facilities
• Virtual museum exhibits that you can seriously
  play with

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How self-explanatorysimulators are built
Students
DomainModeler
Domain Theory
IDE Tools
Scenario
Support Files
Curriculum developer, Teacher, or student
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Compiling self-explanatory simulators
Scenario
Domain Theory
Qualitative Analysis
Qualitative Model
Code Generator
Explanation System
Code
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How the explanation system works

• Simulator keeps track of model fragment activity
  in a concise history
• ltMFi ltstartgt ltendgt ltT,Fgtgt
• ? At any time tick, can recover full activation
  structure
• Causal questions answered by
• Recovering influence graph from activation
  structure
• Filtering results appropriately for audience
• e.g., thermal conductivity not mentioned in
  Evaporation Laboratory
• Cant say, dont tell policy