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Cause in fact Ford v. Trident Fisheries (Mass. 1919) p. 299 How did P’s decedent die? Alleged negligence of trawler’s owners? lifeboat was lashed to deck; delayed rescue only one oar; had to scull Why a directed verdict for D? The court’s reasoning

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ford v trident fisheries mass 1919 p 299
Ford v. Trident Fisheries (Mass. 1919) p. 299
  • How did P’s decedent die?
  • Alleged negligence of trawler’s owners?
    • lifeboat was lashed to deck; delayed rescue
    • only one oar; had to scull
  • Why a directed verdict for D?
the court s reasoning
The court’s reasoning
  • “there is nothing to show they in any way contributed to Ford’s death….that if the boat had been suspended from davits and a different method of propelling it had been used he would have been rescued”
  • why does the court reach that conclusion?
    • No idea where Ford was; he was never seen; no cry heard; no clothing seen; never surfaced?
    • so no reason to believe would have been saved by a speedier craft (Burden of Proof = P)
the but for test of c i f
The “But For” Test of C-I-F
  • Ford’s analysis =called “but for” test
  • P must prove:
    • But for the defendant’s failure to behave reasonably, P would have avoided injury (or suffered less severe injury).
  • Recall Ford:
    • “there is nothing to show.. .that if the boat had been suspended from davits and a different method of propelling it had been used he would have been rescued”
status of the but for test
Status of the “but for” test
  • “But for” = universal starting point for c-I-f
  • If P cannot prove “but for” causation, then loses unless qualifies for an exception
  • “But for” phrasing=may seem awkward
    • try this: “if only” D has taken more precautions, P would not have been injured.
applying the but for test
Applying the “but for” test
  • Brian Dailey pulled the chair. But for?
  • Bargee away from barge?
  • Failure to insulate power line?
    • Was Washington’s decision to move his citizens band radio also a “but for” cause?
  • Having a “rabbit” radio promotion?
    • Was the driving of the teenage defendants also a “but for” cause?
      • What if we don’t know who forced P off road?
relationship to the rs substantial factor test
Relationship to the RS “Substantial Factor” test
  • The RS does not use “but for” language.
  • D’s negligence must be a “substantial factor” in P’s injuries.
    • BUT “substantial factor” is defined to mean that the P prove either:
      • But for, or
      • Exception.
hoyt v jeffers mich 1874 p102
Hoyt v. Jeffers (Mich. 1874)p102
  • What’s the point of the case?
    • Causation can be proven using circumstantial evidence.
  • What if the fire marshal concluded that the fire started in the kitchen?
  • If we rule out the kitchen or cigars, etc., because the fire started on the roof, what does that do to the odds it was D?
  • B/P=“more probable than not”
smith v rapid transit inc mass 1945 p 105
Smith v. Rapid Transit, Inc. (mass. 1945) p. 105
  • Facts?
  • What circumstantial evidence of caustion?
  • Outcome?
  • Why not adequate to go to jury?
    • “not enough that mathematically the chances somewhat favor the proposition”
    • need “actual belief in its truth”
probabilistic proof
Probabilistic Proof
  • Often said the “mere” probabilistic proof is insufficient, standing alone, to create a jury question.
    • Example: Rapid Transit and Slow Transit are only two companies in isolated town. RT runs 97% of the miles. P is hit by unknown bus.
      • Prima facie case against RT? (probably RT?) NO.
    • contrast Hoyt. Circumstantial evidence leads to conclusion that it probably D. [“odds are”!!]
probabilistic proof11
Probabilistic Proof
  • Pointer #1: several exceptions (coming)
  • Pointer #2: often hard to apply
    • what if P knows the bus was blue, like RT?
    • Blue Mercedes bus?
    • Blue Mercedes bus with Asian driver?
    • NB: at some point, no longer just “background odds” but sufficiently particularized to go to jury. (e.g. cracked windshield)
      • Editorial: No clear line because all circumstantial proof is ultimately “probabilistic”.
  • 1. “but for” test--be able to apply
  • 2. sufficiency of the evidence
    • circumstantial=allowed
    • purely probabilistic proof=usually insufficient
  • 3. NEXT: toxic torts
cause in fact 2

Cause-in-Fact #2

Toxic Torts

the causation problem in toxic torts
The causation problem in toxic torts.
  • 1. Ordinary tort: P is negligently hit by a bus. Sues D. Did one of D’s buses cause P’s bad back?
    • 1. Whose bus negligently hit P? (Smith)
    • 2. Did the bus impact cause the injuries of which Plaintiff complains? (“but for”)
      • Example: Pre-existing back problem?
the problem with toxic torts 2
The problem with toxic torts #2
  • 2. Toxic tort. P claims that his lung cancer was caused by exposure to Toxifam, an industrial cleaning solvent.
    • A. Who made the Toxifam-containing solvents used by P’s employer? (Smith)
      • See “market share” liability puzzles later
    • B. Does it cause lung cancer? [NEW ISSUE]
    • C. Is it the most likely cause of P’s lung cancer? (“but for”) [COMPLEX]
does it cause lung cancer
Does it cause lung cancer?
  • P’s typically use medical studies
    • field=“epidemiology”
  • Findings must be “statistically significant”
  • What cause of error in the study does this guard against?
    • The risk that random chance resulted in an atypical sample being used in the study
    • Ex: bowl of 10,000 marbles is 70% black. If we pull
  • Town of Pleasant has 10,000 who voted for Dummy and 8,000 who voted for Liar.
  • Exit pollers want to know who won before votes are counted.
  • Decide to “sample” from people exiting the polling places. Ask 10 people. Assume they are chosen systematically.
    • 6 for Liar; 4 for Dummy
    • Odds that the sample incorrectly identifies the real winner? high
example cont d
Example (cont’d)
  • Researcher knows that his sample may not paint an accurate picture.
  • More likely to be accurate if he picks a larger sample. Say 30. Or 100.
  • When statistically significant?
    • When only 1 chance in 20 (5%) that his sample identifies the wrong winner: (P = .05) )
example again
Example (again)
  • “Confidence intervals” becoming more common that “P” score.
  • Example: Liar leads Dummy, 56-44%.
    • Poller says 95% confident that this is accurately reflects the citizens of New Pleasant, within 3% in either direction. (“plus or minus 3%”= C.I.)
    • L’s lead is more than 3%.
    • So less that 1 chance in 20 that random error led us to pick the wrong winner.
medical example
Medical example
  • Researchers find a relationship between Toxifam and lung cancer.
    • Sampled workers using Toxifam
    • sampled worker not using it.
    • Found 6% of T users get lung cancer
    • Found 2% of others get lung cancer.
    • Statistically significant?
      • If less than 1 chance in twenty that the samples chosen misrepresented reality due to chance.
nonrandom biases
Nonrandom biases
  • D can also question studies that appear to have nonrandom flaws
    • Toxifam workers smoke at higher rate than the general public. That may explain their cancer.
      • Did the study factor this out?.
    • Voters were all polled between 7am and 10 am at one polling place close to Poller’s hotel.
      • Not a representative sample of community.
in the courts
In the courts
  • Test must be statistically significant
    • even though 95% confidence is arguably a more demanding test than “preponderance of the evidence”
  • Nonrandom biases
    • if serious could keep study out of evidence
    • otherwise, go to weight it is given by jury.
did toxifam cause p s cancer
Did Toxifam cause P’s cancer?
  • Can his doctor tell the cause?
  • If not, what are the odds is was Toxifam?
  • Recall the study:
    • 6% of T-workers get lung cancer
    • 2% of others.
  • Most likely cause? How do you know?
  • What’s the minimum % that would prove this?
advanced points
Advanced Points
  • T-3 workers at the Factory get lung cancer.
    • Can they prove causation?
    • How many will recover if they can prove negligence or defective product?
      • How many of the cases did T cause?
  • This paradox can also favor D. For example of T-workers have a 3% cancer rate. None recover despite 50% increase in risk. (Need a doubling dose; I.e. 101% increase).
revisiting probabilistic proof
Revisiting Probabilistic Proof
  • Courts do accept probabilistic proof to prove that
    • the alleged toxin causes harm, and
    • the toxin is the likely cause of P’s injuries
  • Factors that may explain judicial willingness to accept this proof
    • we have identified a tortious defendant (unlike the “whose bus” case)
    • no other way to prove causation (not lazy P)