Exam 1

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32 Terms

1

binary

variables take on only two distinct values

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2

discrete

finite number of values

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3

continuous

infinite number of values

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4

correlation

the extend to which two features of the world occur together

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5

positive correlation

two features of the world occur together

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6

negative correlation

two features of the world move in opposite directions

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7

covariance

measures the direction of the correlation

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8

correlation coefficient

strength of a linear relationship between two variables

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9

regression coefficient

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

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10

description

no assumptions, tells correlations

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11

prediction

data must be representative, no causal story

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12

unbiasedness

would prediction be right on average if we made 1000 predictions

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13

causal inference

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

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14

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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15

causal effect

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

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16

the fundamental problem of causal inference

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

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17

causation

correlation does not imply causation

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18

bias

causal inference problem

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19

noise

statistical inference problem

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20

confounding

something that influences both groups, common cause, reverse causation

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21

estimate

what we see in data, correlation

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22

estimand

what we are interesting in seeing, causal effect

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23

estimator

the procedure we use to generate our estimate

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24

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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25

bias

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

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26

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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27

selection

people choose to be in studies they care about

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28

common cause

behaviors are linked, natural causes, noise

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29

common shocks

behaviors across units are linked

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30

non-random treatment

treatments are linked to characteristics that also affect outcomes

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31

reverse causation

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

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32

E[Y1 - Y0]

average treatment effect for population

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