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Political Science 30 Political Inquiry

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Political science has lots of tricky chicken-and-egg situations. Criterion #2. Correlation Two variables are correlated when changes in one variable ... – PowerPoint PPT presentation

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Title: Political Science 30 Political Inquiry


1
Political Science 30Political Inquiry
  • The Fundamental Problem of Causal Inference
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2
Lecture Outline
  • Confounds and The Fundamental Problem of Causal
    Inference
  • Probabilistic vs. Deterministic Causality
  • Four Criteria for Showing Causality

3
A confounding variable
  • A confound
  • causes changes in the dependent variable
  • is correlated with one of the independent
    variables
  • is causally prior to that independent variable.
    Chronologically or logically, it comes first.
  • Wealth
  • Prior Current
  • Revolution Health Revolution

4
The Fundamental Problem of Causal Inference
  • Problem. We cannot rerun history to see whether
    changing the value of an independent variable
    would have changed the value of the dependent
    variable.
  • Solution 1. Give up.

5
The Fundamental Problem of Causal Inference
  • Solution 2. Design your research in a way that
    comes as close as possible to rerunning history.
  • Observe the effects of changes in one independent
    variable when all other independent variables
    remain the same, or
  • Measure other independent variables, then use
    statistical techniques to hold them constant.

6
Probabilistic vs. Deterministic Causality
(Definitions)
  • Probabilistic means that when the values that
    an IV takes on increase, this usually results in
    the values of the DV increasing (or, usually,
    decreasing)
  • Deterministic means that when the values that
    an IV takes on increase, this always results in
    the values of the DV increasing (or, always,
    decreasing)

7
Why Political Science is Satisfied with a
Probabilistic Notion of Cause
  • Like many other sciences that study complex
    systems, we care about necessary or sufficient
    causal factors that make an effect more likely,
    not just iron laws.
  • More education more likely to vote.
  • Cities that rely more on sales tax more
    likely to subsidize WalMarts
  • Males of a species larger and more
    aggressive than females in a species

8
Four Criteria for Showing Causality
  • 1 Temporal Ordering
  • 2 Correlation
  • 3 Causal Mechanism
  • 4 Rule out Confounds

9
Criterion 1. Temporal Ordering
  • The hypothesized cause (IV) must come before the
    effect (DV).
  • Students decide whether or not to sit in the
    front of class before the get their final grade.
  • Campaign contributions on the eve of an election
    cant cause a Congresswomans voting record in
    the previous session.
  • Political science has lots of tricky
    chicken-and-egg situations.

10
Criterion 2. Correlation
  • Two variables are correlated when changes in
    one variable occur together with changes in the
    other (Louise White)
  • Correlation is roughly synonymous with
    association and co-variance.
  • A correlation between two variables can be
    positive or negative.

11
Criterion 3. Causal Mechanism
  • You have to be able to tell a plausible story
    that connects the IV to the DV
  • This story often includes an intervening
    variable that gets us from the IV to the DV
  • Students who sit up front are able to hear
    better, see better, and better comprehend the
    lecture (plausible story)
  • Students who sit up front of the class absorb
    more of my genius by osmosis (not plausible)

12
Criterion 4. Rule Out Confounds
  • If there is a confound that is causally prior to
    both an IV and a DV, then the correlation we
    observe between the IV and the DV may be
    spurious.
  • A possible confound is that more dedicated
    students are more likely to a. sit up front, and
    b. perform well on the test. The observed
    correlation between their seating choice and
    their performance may be spurious.
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