133 citations · 278 across the 22 of their papers we have counts for
7 papers · 1 filter
Nonparametric Instrumental Variable Inference with Many Weak Instruments
Lars van der Laan, Nathan Kallus, Aurélien Bibaut
We study inference on linear functionals in the nonparametric instrumental variable (NPIV) problem with a discretely-valued instrument under a many-weak-instruments asymptotic regi…
Evaluating Decision Rules Across Many Weak Experiments
Winston Chou, Colin Gray, Nathan Kallus +2
Technology firms conduct randomized controlled experiments ("A/B tests") to learn which actions to take to improve business outcomes. In firms with mature experimentation platforms…
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
Lars van der Laan, Aurelien Bibaut, Nathan Kallus +1
We develop a unified framework for automatic debiased machine learning (autoDML) for inference on a broad class of statistical parameters. The framework applies to any smooth funct…
Anytime-Valid Continuous-Time Confidence Processes for Inhomogeneous Poisson Processes
Michael Lindon, Nathan Kallus
Motivated by monitoring the arrival of incoming adverse events such as customer support calls or crash reports from users exposed to an experimental product change, we consider seq…
Demistifying Inference after Adaptive Experiments
Aurélien Bibaut, Nathan Kallus
Adaptive experiments such as multi-arm bandits adapt the treatment-allocation policy and/or the decision to stop the experiment to the data observed so far. This has the potential…
Learning the Covariance of Treatment Effects Across Many Weak Experiments
Aurélien Bibaut, Winston Chou, Simon Ejdemyr +1
When primary objectives are insensitive or delayed, experimenters may instead focus on proxy metrics derived from secondary outcomes. For example, technology companies often infer…