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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

  • 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?


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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)


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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”


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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.


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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?


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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.


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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”


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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”


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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”!!]


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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”.


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Recap

  • 1. “but for” test--be able to apply

  • 2. sufficiency of the evidence

    • circumstantial=allowed

    • purely probabilistic proof=usually insufficient

  • 3. NEXT: toxic torts


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Cause-in-Fact #2

Toxic Torts


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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?


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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]


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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


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Example

  • 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


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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) )


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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.


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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.


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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.


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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.


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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?


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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).


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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)