A Comparison of Methods for Adaptive Experimentation

07/01/2022
by   Samantha Horn, et al.
0

We use a simulation study to compare three methods for adaptive experimentation: Thompson sampling, Tempered Thompson sampling, and Exploration sampling. We gauge the performance of each in terms of social welfare and estimation accuracy, and as a function of the number of experimental waves. We further construct a set of novel "hybrid" loss measures to identify which methods are optimal for researchers pursuing a combination of experimental aims. Our main results are: 1) the relative performance of Thompson sampling depends on the number of experimental waves, 2) Tempered Thompson sampling uniquely distributes losses across multiple experimental aims, and 3) in most cases, Exploration sampling performs similarly to random assignment.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/14/2020

Deep Learning for ECG Segmentation

We propose an algorithm for electrocardiogram (ECG) segmentation using a...
research
10/25/2022

Adaptive Experimental Design and Counterfactual Inference

Adaptive experimental design methods are increasingly being used in indu...
research
06/02/2018

Rejection Sampling for Tempered Levy Processes

We extend the idea of tempering stable Levy processes to tempering more ...
research
03/18/2021

Optimal soil sampling design based on the maxvol algorithm

Spatial soil sampling is an integral part of a soil survey aimed at crea...
research
01/17/2021

TSEC: a framework for online experimentation under experimental constraints

Thompson sampling is a popular algorithm for solving multi-armed bandit ...
research
04/04/2022

Hybrid Probabilistic-Snowball Sampling

Snowball sampling is the common name for sampling designs on human popul...

Please sign up or login with your details

Forgot password? Click here to reset