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Political Science 30 Political Inquiry. The Fundamental Problem of Causal Inference
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Political Science 30Political Inquiry The Fundamental Problem of Causal Inference To rent SPSS for a PC for $40, go to http://e5.onthehub.com/WebStore/ProductSearchOfferingList.aspx?ws=49c547ba-f56d-dd11-bb6c-0030485a6b08&vsro=8&srch=statistics+base&utm_source=LandingPage-SPSS-Statistics-Base-19&utm_medium=LandingPage&utm_campaign=SPSS
Lecture Outline • Confounds and The Fundamental Problem of Causal Inference • Probabilistic vs. Deterministic Causality • Four Criteria for Showing Causality
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
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.
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.
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)
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
Four Criteria for Showing Causality • #1 Temporal Ordering • #2 Correlation • #3 Causal Mechanism • #4 Rule out Confounds
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 can’t cause a Congresswoman’s voting record in the previous session. • Political science has lots of tricky “chicken-and-egg” situations.
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.
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)
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.