Towards Efficient Statistical Inference and Optimal Design in Adaptive Experiments

Music City Center 

Adaptive experiments play a crucial role in clinical trials and online A/B testing. Unlike static designs, adaptive experiments dynamically adjust treatment randomization probabilities and key elements based on sequentially collected data. This flexibility helps achieve objectives like reducing uncertainty in causal estimates or enhancing participant benefits. However, the adaptive and time-dependent nature of the data collected from such experiments poses challenges for unbiased statistical inference due to non-i.i.d. data. Building upon the Targeted Maximum Likelihood Estimator (TMLE) literature that has provided valid statistical inference tailored to adaptive experimental settings using inverse weighting strategies tailored for adaptive experiment settings, we propose a new TMLE that further improves the efficiency for estimating causal estimands under adaptive designs. Additionally, we present a general framework for implementing optimal adaptive designs tailored to various objectives. We demonstrate the effectiveness of our proposed estimators and adaptive designs through theoretical analysis and extensive simulations.

Adaptive Experimental Design

Targeted Maximum Likelihood Estimation

Causal Inference 

IMS