no code implementations • 29 Feb 2024 • Raj Agrawal, Sam Witty, Andy Zane, Eli Bingham
We prove that MC-EIF is consistent, and that estimators using MC-EIF achieve optimal $\sqrt{N}$ convergence rates.
1 code implementation • 23 Jun 2021 • Raj Agrawal, Tamara Broderick
Often, these effects are nonlinear and include interactions, so linear and additive methods can lead to poor estimation and variable selection.
no code implementations • 17 May 2019 • Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick
Due to the ease of modern data collection, applied statisticians often have access to a large set of covariates that they wish to relate to some observed outcome.
1 code implementation • 16 May 2019 • Raj Agrawal, Jonathan H. Huggins, Brian Trippe, Tamara Broderick
Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines.
3 code implementations • 27 Feb 2019 • Raj Agrawal, Chandler Squires, Karren Yang, Karthik Shanmugam, Caroline Uhler
Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making.
Methodology
no code implementations • 9 Oct 2018 • Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
Random feature maps (RFMs) and the Nystrom method both consider low-rank approximations to the kernel matrix as a potential solution.
1 code implementation • ICML 2018 • Raj Agrawal, Tamara Broderick, Caroline Uhler
Learning a Bayesian network (BN) from data can be useful for decision-making or discovering causal relationships.