A New Framework for Online Testing of Heterogeneous Treatment Effect

Authors: Miao Yu, Wenbin Lu, Rui Song10310-10317

AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We examine the empirical performance of the proposed tests and compare them with a state-of-art online test, named m SPRT using simulations and a real data.
Researcher Affiliation Academia Miao Yu, Wenbin Lu, Rui Song Department of Statistics North Carolina State University {myu12, wlu4, rsong}@ncsu.edu
Pseudocode No The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures).
Open Source Code No The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper.
Open Datasets No The paper mentions using the "Yahoo dataset" but does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for it.
Dataset Splits No The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning.
Hardware Specification No The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments.
Software Dependencies No The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment.
Experiment Setup Yes The significance level α = 0.05, null value of testing parameter β0 = (0, 0) (for 2-dimensional covariates) or (0, 0, 0) (for 3-dimensional covariates) and true nuisance parameter θ0 = (0, 1) (2-dimension) or (0, 1, 1) (3dimension) are fixed for all experiments. Each experiment is repeated 1000 times to estimate type I error and power for SST and m SPRT. In A/B testing, data are generated in batch with batch size 200.