Lecture 2

Cause and Effect

DATA 8

Fall 2018

Slides created by John DeNero (denero@berkeley.edu) and Ani Adhikari (adhikari@berkeley.edu)

Announcements

A Link

Guardian UK

A Stronger Link?

npr.org (report on a study in heart.bmj.com)

Observation

  • individuals, study subjects, participants, units
    • European adults

  • treatment
    • chocolate consumption

  • outcome
    • heart disease

The first question

Is there any relation between chocolate consumption and heart disease?

  • association
    • any relation
    • link

An answer

Some data:


“Among those in the top tier of chocolate consumption, 12 percent developed or died of cardiovascular disease during the study, compared to 17.4 percent of those who didn’t eat chocolate.”
- Howard LeWine of Harvard Health Blog, reported by npr.org

  • Yes, this points to an association

(in my opinion)

The next question

Does chocolate consumption lead to a reduction in heart disease?

  • causality

This question is often harder to answer.

“[The study] doesn’t prove a cause-and-effect relationship between chocolate and reduced risk of heart disease and stroke.”
- JoAnn Manson, chief of Preventive Medicine at Brigham and Women’s Hospital, Boston

Association

London, early 1850’s

Illustration from Punch (1852).

Miasmas, miasmatism, miasmatists

  • Bad smells given off by waste and rotting matter
  • Believed to be the main source of disease
  • Suggested remedies:
    • “fly to clene air”
    • “a pocket full o’posies
    • fire off barrels of gunpowder
  • Staunch believers:
  • Florence Nightingale
    Edwin Chadwick, Commissioner of the General Board of Health

John Snow, 1813-1858

Causation

Comparison

  • treatment group

  • control group
    • does not receive the treatment

Snow’s “Grand Experiment”

“… there is no difference whatever in the houses or the people receiving the supply of the two Water Companies, or in any of the physical conditions with which they are surrounded …”

  • The two groups were similar except for the treatment.

Snow’s table

Supply Area

Number of houses

Cholera deaths

Deaths per 10,000 houses

S&V

40,046

1,263

315

Lambeth

26,107

98

37

Rest of London

256,423

1,422

59

Key to establishing causality

If the treatment and control groups are similar apart from the treatment, then differences between the outcomes in the two groups can be ascribed to the treatment.

Confounding

Trouble

If the treatment and control groups have systematic differences other than the treatment, then it might be difficult to identify causality.

Such differences are often present in observational studies.

When they lead researchers astray, they are called confounding factors.

Example of Confounding

Randomize!

  • If you assign individuals to treatment and control at random, then the two groups are likely to be similar apart from the treatment.

  • You can account – mathematically – for variability in the assignment.

  • Randomized Controlled Experiment

Careful ...

Regardless of what the dictionary says,

in probability theory

Random ≠ Haphazard

Credit: xkcd

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