1. NPHS 1530 Causation, Liability and Correlation
Unit 1 (Part 1)

2. Systems
Components
Environment (weather, terrain, etc.)
People
Hardware (human artifacts)
Software
Data
The amounts and types and design of these components vary. For example, two dramatically different systems can perform the same function.

3. Situation Awareness (SA)
“Situation awareness is the perception of the elements of the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future.” Mica R. Endsley, Human Factors 37 (1995)
A state of knowledge
Situation Assessment: “the process of achieving, acquiring, or maintaining SA [situational awareness].”

4. Path to Situation Awareness: Systems Cycle
Signal - Detection
Data – Measurement
Scales: NOIR (Nominal, Ordinal, Interval, Ratio)
Information – Interpretation
Categories and Nosologies (e.g., diagnoses)
Heuristics
Models (linear and non-linear, agent-based, etc.)
Knowledge - Incorporation
Ontologies (computer/information sciences)

5. Path to Situation Awareness: Systems Cycle
Data – Measurement
Scales: NOIR (Nominal, Ordinal, Interval, Ratio)
Nominal: Descriptors of category membership (e.g., ‘red’, ‘large-beaked’, ‘Yes-No-Don’t Care’ responses).
Ordinal: Ranked or ordered scale (e.g., ‘Adequate-Good-Better-Best’, ‘Often-Sometimes-Seldom-Rarely-Never’)
Interval: Ranked or ordered and the difference has a measure (E.g., temperature scales)
Ratio: There is a natural ‘zero’ and both differences and ratios have meaning

6. Path to Situation Awareness: Systems Cycle
Information – Interpretation
Categories or Nosologies (e.g., Diagnoses)
Heuristics
Empirically-based relationships or solutions to problems (experience, regularity and reliability)
‘Good enough’: rules of thumb, common sense, intuitive judgments, mathematical relations without ‘lawful’ underpinnings
Models (linear and non-linear, agent-based, etc.)
Mechanistic and predictive; may include stochastic error

7. Path to Situation Awareness: Systems Cycle
Knowledge – Incorporation
Ontologies :
a formal representation of knowledge as a set of concepts within a domain, and the relationships between those concepts.
Ontologies are used to reason about the entities within that domain, and may be used to describe the domain.
Hence, they serve as both heuristics and models

8. Path to Situation Awareness: Systems Cycle
Knowledge – Incorporation by ontologies : “…a set of representational primitives with which to model a domain of knowledge or discourse. The representational primitives are typically classes (or sets), attributes (or properties), and relationships (or relations among class members). The definitions of the representational primitives include information about their meaning and constraints on their logically consistent application.”

9. Metadata Descriptive Functional
E.g. for a map – size, scale, name, projection, etc.
Functional
Triggers actions

10. Why Establish Causation?

11. Why Establish Causation?
We think in terms of Cause  Effect relationships for planning and responding
Prevention addresses causes.
Protection addresses effects.
Responding addresses both
Cause  Effect reasoning is component of situation awareness
Cause  Effect reasoning drives ‘Lessons Learned’ operations

12. Causal Inference Limited in National Preparedness and Homeland Security
Limitations on types of data and interpretation
Temporal and spatial resolution
Limited scope: Questionnaires, statistics, etc.
Collateral use of data collected for other purposes
“Which data are relevant?”
Events are often rare and extreme
Some things are ‘unknowable’
Inferences are neither assisted by nor amenable to experimental validation

13. Quote from Macondo Well Blowout Report (Chapter 4, released 6 JAN 11)
…the Macondo blowout was the product of several individual missteps and oversights by BP, Halliburton, and Transocean, which government regulators lacked the authority, the necessary resources, and the technical expertise to prevent.
We may never know the precise extent to which each of these missteps and oversights in fact caused the accident to occur. Certainly we will never know what motivated the final decisions of those on the rig who died that night.

14. Concepts for Identifying Causation
David Hume, An Enquiry on Human Understanding (1748) Chapter VII, Part II:
“We may define a cause to be an object, followed by another, and where all objects similar to the first, are followed by objects similar to the second. Or in other words where, if the first object had not been, the second never had existed.”

15. Concepts for Identifying Causation
David Hume (1): cause defined by invariable patterns of succession (regularity theory)
“We may define a cause to be an object, followed by another, and where all objects similar to the first, are followed by objects similar to the second...”
Necessity (Necessary condition)
Sufficiency (Sufficient condition)

16. Counterfactual Theory and Regularity
David Hume (2): Counterfactual analysis
“Or in other words where, if the first object had not been, the second never had existed.”
Causal Dependence: “Events can be called cause and effect, if the effect always occurs after the cause and if the effect never occurs when the cause is absent.”
The ‘cause’ is both necessary and sufficient for the ‘effect’

17. Concepts for Identifying Causation
The short circuit caused the house to catch fire.
Necessity (Necessary condition)
Sufficiency (Sufficient condition) absent
The short circuit {ignited the open can of gasoline and} caused the house to catch fire.
Sufficiency (Element of a Sufficient Set (ESS) of conditions)

18. Concepts for Identifying Causation
When I roll a ball off the table, it will drop to the floor.
Necessity (Necessary condition)
Sufficiency (Sufficient condition)
When I roll the ball off the table {in a gravitational field} , it will drop to the floor.
When I roll the ball off the table {in a gravitational field and no external opposing force} , it will drop to the floor.

19. Regularity Theories & Deductive-Nomological Model of Scientific Explanation (Hempel)
Deductive Component
Phenomenon must be logical consequence of “sentences adduced to account for the phenomenon”
“Sentences adduced to account for the phenomenon” must be true
Nomological Component
‘Law of Nature’ (at least one) must be an essential premise
Example: Calculate future position of Venus from Newton’s laws of motion, inverse square law for gravity, and data (mass, position) of Venus and sun.

20. Regularity Theories & Inductive-Statistical Model of Scientific Explanation (Hempel)
Inductive Component
Phenomenon must be logical consequence of “sentences adduced to account for the phenomenon” with a high probability
“Sentences adduced to account for the phenomenon” must be true with a high probability
Statistical Component
Probability/risk viewed a lawful behavior
Example: Recovery from disease as a function of clinical data and probability of response to therapy.

21. Example of Deductive-Nomological or Inductive-Statistical Explanations
The impact of truck on the bridge support caused a partial bridge deck collapse.
‘Law’: When trucks impact pillars supporting a section of a bridge and further conditions are met (e.g., mass and velocity of truck, bridge support status, etc.), the bridge deck will partially collapse.
‘Instance’: This truck struck the bridge support and the further conditions were present.
‘Effect’: The bridge deck collapsed partially.

22. Causal Reasoning in Action: Recent Example
FDA Announcement: Serious Concerns Over Alcoholic Beverages with Added Caffeine
Quote: On Nov. 17, 2010, FDA announced that it had sent warning letters to four companies that make malt versions of these beverages, advising them that the caffeine included as a separate ingredient is an “unsafe food additive."

23. Causal Reasoning in Action: Recent Example
Quote: “According to data and expert opinion, caffeine can mask sensory cues that people may rely on to determine how intoxicated they are. This means that individuals drinking these beverages may consume more alcohol—and become more intoxicated—than they realize.  At the same time, caffeine does not change blood alcohol content levels, and thus does not reduce the risk of harms associated with drinking alcohol.” 

24. Causal Reasoning in Action: Recent Example
Argument (part 1): “According to data and expert opinion, caffeine can mask sensory cues that people may rely on to determine how intoxicated they are.”
Caffeine consumption masked sensory cues
Masked sensory cuesimpaired detection of self-intoxication
Which conditions are necessary and which are sufficient?

25. Causal Reasoning in Action: Recent Example
Argument (part 2): “This means that individuals drinking these beverages may consume more alcohol—and become more intoxicated—than they realize.”
Impaired detection of self-intoxication become more intoxicated by drinking more of the beverage  
At the same time, caffeine does not change blood alcohol content levels, and thus does not reduce the risk of harms associated with drinking alcohol.” 
Caffeine consumption does not reduce alcohol risks

26. Causal Reasoning in Action: Recent Example
Argument (Part 3): “Studies suggest that drinking caffeine and alcohol together may lead to hazardous and life-threatening behaviors.  For example, serious concerns are raised about whether the combination of alcohol and caffeine is associated with an increased risk of alcohol-related consequences, including alcohol poisoning, sexual assault, and riding with a driver who is under the influence of alcohol.” 

27. Is regularity theory realistic?
Imperfect regularity: “Smoking is a cause of lung cancer.”
Irrelevance: “Salt that has been hexed by a sorcerer invariable dissolves when placed in water.”
Asymmetry: “Smoking is a cause of lung cancer but lung cancer does not cause one to smoke.”
Spurious regularities: “A rapid drop in barometric pressure  (1) drop in a column of mercury and (2) a storm (with a short delay).”

28. Spurious Irregularities: Other Common Erroneous Causal Conclusions
Trailer parks attract tornadoes.
Lightning never strikes in the same place twice.

29. Macondo Well Blowout Report (Chapter 4, released 6 JAN 11)
…the Macondo blowout was the product of several individual missteps and oversights by BP, Halliburton, and Transocean, which government regulators lacked the authority, the necessary resources, and the technical expertise to prevent.
We may never know the precise extent to which each of these missteps and oversights in fact caused the accident to occur. Certainly we will never know what motivated the final decisions of those on the rig who died that night.
What we nonetheless do know is considerable and significant: (1) each of the mistakes made on the rig and onshore by industry and government increased the risk of a well blowout; (2) the cumulative risk that resulted from these decisions and actions was both unreasonably large and avoidable; and (3) the risk of a catastrophic blowout was ultimately realized on April 20 and several of the mistakes were contributing causes of the blowout.

30. Macondo Well Blowout Report (Chapter 4, released 6 JAN 11)
The immediate cause of the Macondo blowout was a failure to contain hydrocarbon pressures in the well. Three things could have contained those pressures: the cement at the bottom of the well, the mud in the well and in the riser, and the blowout preventer.
But mistakes and failures to appreciate risk compromised each of those potential barriers, steadily depriving the rig crew of safeguards until the blowout was inevitable and, at the very end, uncontrollable.