Cause in fact - PowerPoint PPT Presentation

cause in fact l.
Skip this Video
Loading SlideShow in 5 Seconds..
Cause in fact PowerPoint Presentation
Download Presentation
Cause in fact

play fullscreen
1 / 25
Download Presentation
Cause in fact
Download Presentation

Cause in fact

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Cause in fact

  2. 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?

  3. 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)

  4. 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”

  5. 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.

  6. 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?

  7. 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.

  8. 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”

  9. 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”

  10. 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”!!]

  11. 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”.

  12. 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

  13. Cause-in-Fact #2 Toxic Torts

  14. 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?

  15. 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]

  16. 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

  17. 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

  18. 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) )

  19. 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.

  20. 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.

  21. 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.

  22. 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.

  23. 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?

  24. 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).

  25. 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)