Jiannan Lu | DeepAI

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∙ 05/25/2023

All about sample-size calculations for A/B testing: Novel extensions and practical guide

While there exists a large amount of literature on the general challenge...

0 Jing Zhou, et al. ∙

∙ 03/17/2023

Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out

With the growing popularity of intelligent assistants (IAs), evaluating ...

0 Zidi Xiu, et al. ∙

∙ 08/25/2022

Addressing Hidden Imperfections in Online Experimentation

Technology companies are increasingly using randomized controlled trials...

0 Jeffrey Wong, et al. ∙

∙ 05/31/2021

The equivalence of the Delta method and the cluster-robust variance estimator for the analysis of clustered randomized experiments

It often happens that the same problem presents itself to different comm...

0 Alex Deng, et al. ∙

∙ 02/18/2021

Novelty and Primacy: A Long-Term Estimator for Online Experiments

Online experiments are the gold standard for evaluating impact on user e...

0 Soheil Sadeghi, et al. ∙

∙ 10/09/2019

On Post-Selection Inference in A/B Tests

When a large number of simultaneous statistical inferences are conducted...

0 Alex Deng, et al. ∙

∙ 07/24/2019

Sharp bounds on the relative treatment effect for ordinal outcomes

For ordinal outcomes, the average treatment effect is often ill-defined ...

0 Jiannan Lu, et al. ∙

∙ 02/06/2019

Heavy User Effect in A/B Testing: Identification and Estimation

On-line experimentation (also known as A/B testing) has become an integr...

0 Yu Wang, et al. ∙

∙ 03/16/2018

Applying the Delta method in metric analytics: A practical guide with novel ideas

During the last decade, the information technology industry has adopted ...

0 Alex Deng, et al. ∙

∙ 03/12/2018

On improving Neymanian analysis for 2^K factorial designs with binary outcomes

2^K factorial designs are widely adopted by statisticians and the broade...

0 Jiannan Lu, et al. ∙

∙ 03/12/2018

On finite-population Bayesian inferences for 2^K factorial designs with binary outcomes

Inspired by the pioneering work of Rubin (1978), we employ the potential...

0 Jiannan Lu, et al. ∙

∙ 11/13/2017

Sharpening randomization-based causal inference for 2^2 factorial designs with binary outcomes

In medical research, a scenario often entertained is randomized controll...

0 Jiannan Lu, et al. ∙

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