Conditional probability distribution

Bivariate Probability Distributions and Sampling Distributions

Published in William M. Mendenhall, Terry L. Sincich, Statistics for Engineering and the Sciences, 2016

William M. Mendenhall, Terry L. Sincich

The conditional probability distribution for a random variable X, given Y, is the joint probability distribution for X and Y divided by the marginal probability distribution for Y.

A causal inference method for canal safety anomaly detection based on structural causal model and GBDT

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Journal Information

Published in LHB, 2023

Hairui Li, Xuemei Liu, Xianfeng Huai, Xiaolu Chen

Since the need for prediction is prevalent in big data applications, prediction is often used as the goal of model estimation in regression modelling. However, research in recent years has found that machine learning models can achieve good results by learning pseudo-correlation, but do not generalise well in real-world settings, making these problems more appropriate as causal inference tasks. The causal inference task is somewhat similar to the prediction task in that both are based on some evidence that gives an estimate of a variable. The difference between prediction and causal inference lies in the different modelling objectives. The prediction task is to learn the conditional probability distribution from historical data by simply considering the correlation between features x and variables y, which is used to estimate the value of y that fits the pattern of historical data given x. The causal inference task, on the other hand, is to learn the intervention distribution among variables from historical data under certain causality assumptions, which is usually used to estimate the effect of a change in x on y.

Conditional probability distribution

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Bivariate Probability Distributions and Sampling Distributions

A causal inference method for canal safety anomaly detection based on structural causal model and GBDT