Exam 1

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32 Terms

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binary

variables take on only two distinct values

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discrete

finite number of values

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continuous

infinite number of values

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correlation

the extend to which two features of the world occur together

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positive correlation

two features of the world occur together

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negative correlation

two features of the world move in opposite directions

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covariance

measures the direction of the correlation

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correlation coefficient

strength of a linear relationship between two variables

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regression coefficient

how much Y changes on average when x changes by one unit

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description

no assumptions, tells correlations

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prediction

data must be representative, no causal story

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unbiasedness

would prediction be right on average if we made 1000 predictions

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causal inference

how does changing a feature of the world change some other feature, need random assignment, research and design

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counterfactual dependence

X causes Y if and only if Y occurs when X occurs and Y would not have occurred if X did not occur

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causal effect

if Y1 - Y0 does not equal 0, X has a _ on Y

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the fundamental problem of causal inference

we never observe Y1 - Y0 for a person about only one is observable

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causation

correlation does not imply causation

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bias

causal inference problem

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noise

statistical inference problem

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confounding

something that influences both groups, common cause, reverse causation

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estimate

what we see in data, correlation

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estimand

what we are interesting in seeing, causal effect

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estimator

the procedure we use to generate our estimate

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unbiasedness

estimator is unbiased if by repeated our estimation procedure over and over again an infinite number of times the average value of estimates would equal estimated

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bias

E[Y0|T = 1] - E[YO|T = 0]

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apples to apples

YO and Y1 needs to be good substitutions and we need no difference on average between treatment and control groups

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selection

people choose to be in studies they care about

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common cause

behaviors are linked, natural causes, noise

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common shocks

behaviors across units are linked

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non-random treatment

treatments are linked to characteristics that also affect outcomes

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reverse causation

occurs when you believe that X causes Y, but in reality Y actually causes X

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E[Y1 - Y0]

average treatment effect for population