Tag：Experience group
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Time：2020-9-2
This article starts with WeChat official account of vivo Internet technology.Link: https://mp.weixin.qq.com/s/mO5MdwG7apD6RzDhFwZhogAuthor: duzhimin More and more companies are trying abtest, either building their own systems or relying on third-party systems. So what are the necessary basic knowledge when we conduct abtest? How to carry out AB experiment step by step? In this paper, we will […]
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Time：2020-7-23
The purpose of heterogeneous treatment effect is to quantify the differential effects of experiments on different populations, and then to conduct differential experiments or adjust experiments through crowd oriented / numerical strategies. Double machine learning takes treatment as a feature, and calculates the difference effect of the experiment by estimating the influence of the feature […]
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Time：2020-7-9
Meta learner is different from the causal tree direct estimation model introduced before, and belongs to one of the indirect estimation models. It does not directly model the treatment effect, but models the response effect (target), and uses the change of target caused by treatment as the estimation of hte. There are three main methods: […]
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Time：2020-6-9
CACE full name compiler average casual effect or local average treatment effect. The application in the observation data needs to be combined with the instrument variable. Here we only discuss some learning provided by the CACE framework for random AB experiments. Have you ever encountered the following low experimental permeability? The new function entry is […]
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Time：2019-11-21
background It is said that random is the core of AB experiment. Why is random so important? It is said that there is no difference in AB group as a whole because of randomization, so as to accurately estimate the experimental effect (ATE) \[ ATE = E(Y_t(1) – Y_c(0)) \] How is random defined According […]
