Bonus

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>

EditionsHeardPPBEasy %Medium %Hard %
15811.3897%16%2%

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Conversion

TeamOpponentPart 1Part 2Part 3TotalParts
BUUMass Boston001010E
Dartmouth ABrown A001010E
Harvard ABrandeis A10101030HME
Harvard BBrandeis B001010E
MIT ATufts A001010E

Summary

TournamentEditionHeardPPBEasy %Medium %Hard %
California2025-02-0136.6767%0%0%
Florida2025-02-0136.6767%0%0%
Lower Mid-Atlantic2025-02-01611.67100%17%0%
Midwest2025-02-01610.00100%0%0%
North2025-02-01310.00100%0%0%
Northeast2025-02-01514.00100%20%20%
Overflow2025-02-01512.00100%20%0%
South Central2025-02-01215.00100%50%0%
Southeast2025-02-01410.00100%0%0%
UK2025-02-011012.00100%20%0%
Upper Mid-Atlantic2025-02-01812.50100%25%0%
Upstate NY2025-02-01313.33100%33%0%