Unit A2.1 Causality

Unit A2.1 CausalityKenneth D. ForbusQualitative Reasoning GroupNorthwestern University

OverviewWhat is causality?Design choices for causality in qualitative physicsUsing causalityExample: Self-explanatory simulators

A qualitative physics view of causationThere are several broadly used notions of causality in reasoning about the physical worldThey can be decomposed by several factors, includingOntological assumptions: Is there a class of entities that act as mechanisms in the domain?Measurement scenario: What sense of change is being discussed?

Measurement Scenarios affect causalityIncrementalCause precedes effectContinuous Cause, effect coextensiveHeat flow causesheat of water to rise,which causes temperature of water to riseMoving soup spoon causesthe napkin to wipe your face

Implications for theories of causal reasoningConsider 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 domaincf. use of processes versus components in thermodynamics versus electronics No single, simple account of causality is sufficient. Gold standard is psychology, not physics

Causality via PropagationSource 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

Mythical CausalityWhat a system does between quasistatic statesExtremely short period of time within which incremental causality operates, even in continuous systemsMotivation: Capture intuitive explanations of experts about causality in continuous systems, without violating philosophical ideas such as A Cause must precede its effect

Implications of causality as propagationIdentifies order of causality with order of computation.No input no causalityQuantitative analog: Simulators like SPICE require an order of computation to drive them.

Causality in QP theory(Forbus, 1981; 1984)Sole Mechanism assumption: All causal changes stem from physical processesChanges propagate from quantities directly influenced by processes through causal laws to indirectly influenced quantitiesNaturally models human reasoning in many domains (i.e., fluids, heat, motion)Liquid Flow F GI-I+

Implications of Sole Mechanism assumptionAll natural changes must be traced back to the action of some physical processIf not so explained, either an agent is involved, or a closed-world assumption is incorrectThe scenario isnt fully or accurately knownThe reasoners process vocabulary is incomplete or incorrectSyntactic enforcement: Direct influences only appear in descriptions of physical processesCausal direction in qualitative relations crucial for ensuring correct causal explanations

How directional are causal laws?Answer: It dependsIn some domains, clear causal direction across broad variety of situationscf. engineering thermodynamicsIn some domains, causal direction varies across broad variety of situationscf. analog electronics

T =f(heat, mass, )V = I * R

Causal Ordering Used by H. Simon in economics in 1953InputsSet of equations (quantitative or qualitative)Subset of parameters identified as exogenousOutputDirected graph of causal relationshipsMethod (informal)Exogenous parameters comprise starting set of explained parametersFind all equations that have exactly one parameter not yet explained.Add causal links from explained parameters to the unexplained parameterAdd unexplained parameter to set of explained parametersContinue until exhausted

Tradeoffs in causal ordering algorithmAdvantagesCan provide causal story for any set of equations Assuming well-formed and enough exogenous parametersCausal story can change dynamically if what is exogenous changes

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

Self-Explanatory SimulatorsIdea: Integrate qualitative and numerical representations to achievePrecision and speed of numerical simulationExplanatory power of qualitative physicsImagineSimEarth with explanationsInteractive, active illustrations in textbooksTraining simulators with debriefing facilitiesVirtual museum exhibits that you can seriously play with

How self-explanatorysimulators are builtDomainTheoryScenarioIDE & ToolsSupportFilesStudentsDomain ModelerCurriculum developer, Teacher, orstudent

DomainTheoryScenarioQualitativeModelCodeExplanationSystemQualitativeAnalysisCodeGeneratorCompiling self-explanatorysimulators

How the explanation system worksSimulator keeps track of model fragment activity in a concise history At any time tick, can recover full activation structureCausal questions answered byRecovering influence graph from activation structureFiltering results appropriately for audiencee.g., thermal conductivity not mentioned in Evaporation LaboratoryCant say, dont tell policy

Rube Goldberg walks in his sleep, strolls through a cactus field in his bare feet, and screams out an idea for self-operating napkin: As youraise spoon of soup (A) to your mouth it pulls string (B), thereby jerkingladle (C) which throws cracker (D) past parrot (E). Parrot jumpsafter cracker and perch (F) tilts, upsetting seeds (G) into pail (H).Extra weight in pail pulls cord (I), which opens and lights automatic cigarlighter (J), setting off sky-rocket (K) which causes sickle (L) to cut string (M) and allow pendulum with attached napkin to swing back andforth thereby wiping off your chin. After the meal, substitute a harmonica for the napkin and you'll be able to entertain the guests with a littlemusic