36 citations · 46 across the 6 of their papers we have counts for
7 papers
Optimal Variance Reduction in Randomized Experiments
Amir Najmi, Michael D. Keselman
This paper describes an approach to variance reduction in randomized experiments using side information (covariates of the response unaffected by treatment). As with Double ML[Cher…
The Penalty Imposed by Ablated Data Augmentation
Frederick Liu, Amir Najmi, Mukund Sundararajan
There is a set of data augmentation techniques that ablate parts of the input at random. These include input dropout, cutout, and random erasing. We term these techniques ablated d…
The many Shapley values for model explanation
Mukund Sundararajan, Amir Najmi
The Shapley value has become a popular method to attribute the prediction of a machine-learning model on an input to its base features. The use of the Shapley value is justified by…
Unbiased variance reduction in randomized experiments
Reza Hosseini, Amir Najmi
This paper develops a flexible method for decreasing the variance of estimators for complex experiment effect metrics (e.g. ratio metrics) while retaining asymptotic unbiasedness.…
Randomized Experimental Design via Geographic Clustering
David Rolnick, Kevin Aydin, Jean Pouget-Abadie +3
Web-based services often run randomized experiments to improve their products. A popular way to run these experiments is to use geographical regions as units of experimentation, si…
Second Order Calibration: A Simple Way to Get Approximate Posteriors
Omkar Muralidharan, Amir Najmi
Many large-scale machine learning problems involve estimating an unknown parameter for each of many items. For example, a key problem in sponsored search is to estimate the…
Feedback Detection for Live Predictors
Stefan Wager, Nick Chamandy, Omkar Muralidharan +1
A predictor that is deployed in a live production system may perturb the features it uses to make predictions. Such a feedback loop can occur, for example, when a model that predic…