106 citations · 636 across the 106 of their papers we have counts for
18 papers · 1 filter
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…
CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference
Jiyuan Tan, Vasilis Syrgkanis
Automating theoretical research requires generating candidate results and evaluating them reliably. Models keep getting better at the first, while the second remains hard. A common…
Adaptive Estimation and Inference in Conditional Moment Models via the Discrepancy Principle
Jiyuan Tan, Vasilis Syrgkanis
We study adaptive estimation and inference in ill-posed linear inverse problems defined by conditional moment restrictions. Existing regularized estimators such as Regularized Deep…
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…
Sharp Structure-Agnostic Lower Bounds for General Linear Functional Estimation
Jikai Jin, Vasilis Syrgkanis
We establish a general statistical optimality theory for estimation problems where the target parameter is a linear functional of an unknown nuisance component that must be estimat…
It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
Jikai Jin, Lester Mackey, Vasilis Syrgkanis
Structure-agnostic causal inference studies how well one can estimate a treatment effect given black-box machine learning estimates of nuisance functions (like the impact of confou…