13 Experiments

Question 1

differences estimator

Answer 1

E(β1) = E[Yi | Xi = 1] − E[Yi | Xi = 0]

Question 2

Conditional Mean Independence

Answer 2

Conditional Mean Independence:
E[ui | Xi , Wi] = E[ui | Wi] != 0

This is unproblematic as long as we are only interested in the causal effect of Xi and not in the causal effect of Wi:

Under Conditional Mean Independence, OLS will give an unbiased estimate of the causal effect of Xi

Question 3

Threats to internal validity during ideal randomized experiments

Answer 3

Failure to randomize
Failure to follow the treatment protocol
Attrition
Hawthorne effect
Small samples

Question 4

The treatment might not be assigned randomly but instead is based on characteristics or preferences of the subjects

If this is due to the fact that the experimenter assigned the treatment randomly conditional on observed characteristics…
• .

Answer 4

The treatment might not be assigned randomly but instead is based on characteristics or preferences of the subjects

If this is due to the fact that the experimenter assigned the treatment randomly conditional on observed characteristics…

…we can estimate the causal effect by including these observed characteristics in the regression (conditional mean independence)

Question 5

If the treatment is randomly assigned conditional on unobserved characteristics or preferences…

Answer 5

If the treatment is randomly assigned conditional on unobserved characteristics or preferences…

…the estimated treatment “effect” will reflect both the effect of the treatment and the effect of these unobserved characteristics.

Question 6

Double-blind experiment

Answer 6

Subjects and experimenters know that they are in an experiment, but neither know which subjects are in the treatment group and which in the control group.