6 papers
Inference on the Value of Policy Improvement in Adaptive Experiments
Justin Whitehouse, Emma Brunskill, Vasilis Syrgkanis
In this paper, we develop an asymptotically normal estimator for the value of policy improvement (VPI) under adaptively collected data. This value, which is defined as the gap betw…
Measuring Gift Card Program Incrementality via Causal Data Fusion
Justin Whitehouse, William Betz, Yan Zhang +3
Businesses regularly offer gift card programs to drive customer spending and increase engagement. A central question is how much incremental revenue these programs generate, and wh…
Statistical Inference and Learning for Shapley Additive Explanations (SHAP)
Justin Whitehouse, Ayush Sawarni, Vasilis Syrgkanis
The SHAP (short for Shapley additive explanation) framework has become an essential tool for attributing importance to variables in predictive tasks. In model-agnostic settings, SH…
Policy Learning with Abstention
Ayush Sawarni, Jikai Jin, Justin Whitehouse +1
Policy learning algorithms are widely used in areas such as personalized medicine and advertising to develop individualized treatment regimes. However, most methods force a decisio…
Doubly-Robust LLM-as-a-Judge: Externally Valid Estimation with Imperfect Personas
Luke Guerdan, Justin Whitehouse, Kimberly Truong +2
As Generative AI (GenAI) systems see growing adoption, a key concern involves the external validity of evaluations, or the extent to which they generalize from lab-based to real-wo…
Inference on Optimal Policy Values and Other Irregular Functionals via Softmax Smoothing
Justin Whitehouse, Qizhao Chen, Morgane Austern +1
Constructing confidence intervals for the value of an (unknown) optimal treatment policy is a fundamental problem in causal inference. Insight into the optimal policy value can gui…