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- Joint Institute for Nuclear ResearchRU433 papers
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- Massachusetts Institute of TechnologyUS406 papers
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36 papers · 1 filter
Cause-effect inference through spectral independence in linear dynamical systems: theoretical foundations
Michel Besserve, Naji Shajarisales, Dominik Janzing +1
Distinguishing between cause and effect using time series observational data is a major challenge in many scientific fields. A new perspective has been provided based on the princi…
maars: Tidy Inference under the 'Models as Approximations' Framework in R
Riccardo Fogliato, Shamindra Shrotriya, Arun Kumar Kuchibhotla
Linear regression using ordinary least squares (OLS) is a critical part of every statistician's toolkit. In R, this is elegantly implemented via lm() and its related functions. How…
When the Oracle Misleads: Modeling the Consequences of Using Observable Rather than Potential Outcomes in Risk Assessment Instruments
Alan Mishler, Niccolò Dalmasso
Risk Assessment Instruments (RAIs) are widely used to forecast adverse outcomes in domains such as healthcare and criminal justice. RAIs are commonly trained on observational data…
Randomized tests for high-dimensional regression: A more efficient and powerful solution
Yue Li, Ilmun Kim, Yuting Wei
We investigate the problem of testing the global null in the high-dimensional regression models when the feature dimension grows proportionally to the number of observations $n…
On Testing for Biases in Peer Review
Ivan Stelmakh, Nihar B. Shah, Aarti Singh
We consider the issue of biases in scholarly research, specifically, in peer review. There is a long standing debate on whether exposing author identities to reviewers induces bias…
Causal Inference for Comprehensive Cohort Studies
Yi Lu, Daniel O. Scharfstein, Maria M. Brooks +2
In a comprehensive cohort study of two competing treatments (say, A and B), clinically eligible individuals are first asked to enroll in a randomized trial and, if they refuse, are…