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The debate was neither tobacco nor cancer. It was the word “cause”. Many people smoke their whole lives and never get lung cancer. Some get it without lighting up a single cigarette. Previously when we discussed RCT, we know that it is an excellent way to establish a causal relationship.

To better understand this article, you need to first read these two articles: Causal Diagram and Bayesian Network (Part 2) How Randomized Controlled Trial Deconfounds the Confounding Bias A confounder is like some backdoor paths from X to Y. To eliminate the confounder, you just need to block those backdoor

Before talking about how confounding should be defined, let’s talked about how it was defined before. Some declarative defintions A confounder is any variable that is correlated with both X and Y. A confounder X and Y is a variable Z that is (1) associated with X in the

What is confunding bias? Imagine you are a farmer trying to determine applying fertilizer A to the field will increase the yield of crops. In other words, you are trying to establish a causal relationship between fertilizer A and yield. Our tricky Nature tells you that the effect of the

There are three building blocks of Baysian Network. Remember that a Bayesian Network is nothing but some arrows connecting with different nodes representing the conditional probability table. The so-called building blocks are just the three types of arrow patterns. Arrow PatternEasy To RMB NameDescriptionA –> B –> CChainFire ->