Which research method is used to determine causality

Natural Language Processing In Finance : Forecasting Stock Price Changes Using Natural Language ProcessingД«y training the advertising model, we can understand the user characteristics, user context, previous interaction and advertising attributes, and then learn to predict the most likely ads that users click on the website. Advertising systems use machine learning to determine what kind of ads are displayed to specific users. Later, when users visit Facebook, the model generates the best push from thousands of candidates, which is a personalized collection of images and other content and the best ranking of selected content. The general model will determine the various user and environmental factors that affect the content ranking through training. Message push ranking algorithm enables users to see the most important thing to them first when they visit Facebook. Information push, advertisement and facer are the main service objects of machine learning in Facebook. FacebookвЂ™s two billion users use machine learning every day, but they donвЂ™t realize it. The recommendation that this method often brings inadvertently meets customersвЂ™ optional but unnecessary needs and brings more consumption for Amazon.ДЇacebook is also a company that uses machine learning to the extreme. The machine learning method will recommend the products you may need according to the purchase and browsing records you did on Amazon in the past. Machine learning is used to determine user preferences (such as product purchases), as well as Alexa engine, Alexa smart home devices, Amazon JHIM, Amazon Rekognition, Amazon music, and other functions. Amazon, for instance, uses machine learning for all consumer services, from online stores to kindle and echo devices. Most of them use machine learning methods to study past information, to make predictions. Take a few machine learning companies for example. Look-ahead bias exists if the model uses data not available to market participants at the time the market participants act in the model.ДЇinally, time-period bias is present if the time period used makes the results time-period specific or if the time period used includes a point of structural change. Data-mining bias comes from finding models by repeatedly searching through databases for patterns. Survivorship bias occurs if companies are excluded from the analysis because they have gone out of business or because of reasons related to poor performance. Sampling error will bring prediction error. Therefore, sampling is particularly important. According to the new information, our understanding of unknown parameters also changes, this change is reflected in the posterior probability. After sampling, the sample information brings new information.

In forecasting, we usually sample the population sample, and the prior probability reflects our understanding of the unknown parameters before sampling. Thus, the internal causality and logic will be more objective and close when the posterior probability is used to predict. The posterior probability distribution integrates the sample information and the prior probability distribution, which is more objective. Just like the posterior probability in Bayesian law, it is different from the prior probability in that the prior probability distribution does not have to have an objective basis, it can be partially or completely based on subjective consciousness. Generally speaking, prediction is to infer the future based on what happened in history. Regression analysisВ is one of the analytical methods reflecting the causal relationship between the explained variable and the explanatory variable.

Also, there are many sophisticated ways in Probability and Statistics to examine that. There are many ways of performing regression analysis, not only one formula, causal relationships are not necessarily measured by regression. Causal Reasoning & Artificial intelligence

Which research method is used to determine causality