Per a model named for this statistician, the fundamental problem of causal inference states that it is impossible to observe the effect of more than one treatment on a subject and to directly observe causal effects. For 10 points each:
[10h] Name this Harvard statistician whose causal model uses Jerzy Neyman’s (“YAIR-zhih NAY-min’s”) potential outcomes framework and a probabilistic assignment mechanism to estimate missing counterfactuals.
ANSWER: Donald Rubin [or Donald Bruce Rubin]
[10m] When non-compliers exist, these variables can be used to estimate causal relationships. These variables must be correlated with endogenous explanatory variables and are subject to the exclusion restriction.
ANSWER: instrumental variables [or IV]
[10e] Non-compliance can necessitate adjusting for post-treatment covariates by using the stratified form of this technique. This technique involves selecting individuals at random from a population.
ANSWER: sampling [accept stratified sampling]
<Chicago B, Social Science>