abstract | booktitle | title | year | layout | series | publisher | issn | id | month | tex_title | firstpage | lastpage | page | order | cycles | bibtex_author | author | date | address | container-title | volume | genre | issued | extras | |||||||||||||||||||||
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In order to test if a treatment is perceptibly different from a placebo in a randomized experiment with covariates, classical nonparametric tests based on ranks of observations/residuals have been employed (eg: by Rosenbaum), with finite-sample valid inference enabled via permutations. This paper proposes a different principle on which to base inference: if — with access to all covariates and outcomes, but without access to any treatment assignments — one can form a ranking of the subjects that is sufficiently nonrandom (eg: mostly treated followed by mostly control), then we can confidently conclude that there must be a treatment effect. Based on a more nuanced, quantifiable, version of this principle, we design an interactive test called i-bet: the analyst forms a single permutation of the subjects one element at a time, and at each step the analyst bets toy money on whether that subject was actually treated or not, and learns the truth immediately after. The wealth process forms a real-valued measure of evidence against the global causal null, and we may reject the null at level |
First Conference on Causal Learning and Reasoning |
Interactive rank testing by betting |
2022 |
inproceedings |
Proceedings of Machine Learning Research |
PMLR |
2640-3498 |
duan22a |
0 |
Interactive rank testing by betting |
201 |
235 |
201-235 |
201 |
false |
Duan, Boyan and Ramdas, Aaditya and Wasserman, Larry |
|
2022-06-28 |
Proceedings of the First Conference on Causal Learning and Reasoning |
177 |
inproceedings |
|