72 citations · 123 across the 12 of their papers we have counts for
15 papers · 1 filter
Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters
Abhinandan Dalal, Patrick Blöbaum, Shiva Kasiviswanathan +1
Double (debiased) machine learning (DML) has seen widespread use in recent years for learning causal/structural parameters, in part due to its flexibility and adaptability to high-…
Distribution-free calibration guarantees for histogram binning without sample splitting
Chirag Gupta, Aaditya K. Ramdas
We prove calibration guarantees for the popular histogram binning (also called uniform-mass binning) method of Zadrozny and Elkan [2001]. Histogram binning has displayed strong pra…
The leave-one-covariate-out conditional randomization test
Eugene Katsevich, Aaditya Ramdas
Conditional independence testing is an important problem, yet provably hard without assumptions. One of the assumptions that has become popular of late is called "model-X", where w…
Confidence sequences for sampling without replacement
Ian Waudby-Smith, Aaditya Ramdas
Many practical tasks involve sampling sequentially without replacement (WoR) from a finite population of size , in an attempt to estimate some parameter . Accurately qu…
Familywise Error Rate Control by Interactive Unmasking
Boyan Duan, Aaditya Ramdas, Larry Wasserman
We propose a method for multiple hypothesis testing with familywise error rate (FWER) control, called the i-FWER test. Most testing methods are predefined algorithms that do not al…
The Power of Batching in Multiple Hypothesis Testing
Tijana Zrnic, Daniel L. Jiang, Aaditya Ramdas +1
One important partition of algorithms for controlling the false discovery rate (FDR) in multiple testing is into offline and online algorithms. The first generally achieve signific…