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Last time, we have talked about Monty Hall Problem and Berkson’s Paradox. This time, let’s discuss another paradox called Simpson’s Paradox. Let’s consider the following table: This is not a randomized experiment. The data is observed. By looking at the data, you can observe the following:

I want to illustrate this problem using a causal diagram. Here is the rule of the game: Suppose you’re on a game show, and you’re given the choice of three doors. Behind one door is a car, behind the others, goats. You pick a door, say #1, and

In the last post, I have shared about a debate: Does smoking cause lung cancer? A case on causation v.s. correlation Today, I would like to show the proof on Corenfield’s Inequality, an inequality that settled the debate. I have read the original proof on the paper. Although

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