Kernel Treatment Effects with Adaptively Collected Data
Adaptive experiments improve efficiency by adjusting treatment assignments based on past outcomes, but this adaptivity breaks the i.i.d.\ assumptions that underpin classical asymptotics. At the same time, many questions of interest are distributional, extending beyond average effects. Kernel treatment effects (KTE) provide a flexible framework by representing interventional outcome distributions in an RKHS and comparing them via kernel distances. We present the first kernel-based framework for distributional inference under adaptive data collection. Our method combines doubly robust RKHS scores with a witness function learned on one fold, and performs inference on a second fold using a projected, sequentially normalized scalar statistic with valid type-I error. Experiments show that the resulting procedure is well calibrated and effective for both mean shifts and higher-moment differences, outperforming adaptive baselines limited to scalar effects.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Why Adaptively Collected Data Have Negative Bias and How to Correct for It
From scientific experiments to online A/B testing, the previously observed data often affects how future experiments are performed, which in turn affects which data will be collected. Such adaptivity introduces complex c…
An Efficient Doubly-Robust Test for the Kernel Treatment Effect
The average treatment effect, which is the difference in expectation of the counterfactuals, is probably the most popular target effect in causal inference with binary treatments. However, treatments may have effects bey…
Causal InferencevalidCausal Clustering for Conditional Average Treatment Effects Estimation and Subgroup Discovery
Estimating heterogeneous treatment effects is critical in domains such as personalized medicine, resource allocation, and policy evaluation. A central challenge lies in identifying subpopulations that respond differently…
Causal InferenceOff-policy estimation with adaptively collected data: the power of online learning
We consider estimation of a linear functional of the treatment effect using adaptively collected data. This task finds a variety of applications including the off-policy evaluation (\textsf{OPE}) in contextual bandits, a…
Causal InferenceMulti-Armed BanditsOff-policy evaluationKernel Optimal Orthogonality Weighting: A Balancing Approach to Estimating Effects of Continuous Treatments
Many scientific questions require estimating the effects of continuous treatments. Outcome modeling and weighted regression based on the generalized propensity score are the most commonly used methods to evaluate continu…