Linked Questions

77
votes
7answers
15k views

The Book of Why by Judea Pearl: Why is he bashing statistics?

I am reading The Book of Why by Judea Pearl, and it is getting under my skin1. Specifically, it appears to me that he is unconditionally bashing "classical" statistics by putting up a straw man ...
42
votes
5answers
9k views

Is machine learning less useful for understanding causality, thus less interesting for social science?

My understanding of the difference between machine learning/other statistical predictive techniques vs. the kind of statistics that social scientists (e.g., economists) use is that economists seem ...
38
votes
3answers
4k views

Does statistical independence mean lack of causation?

Two random variables A and B are statistically independent. That means that in the DAG of the process: $(A {\perp\!\!\!\perp} B)$ and of course $P(A|B)=P(A)$. But does that also mean that there's no ...
51
votes
3answers
5k views

Statistics and causal inference?

In his 1984 paper "Statistics and Causal Inference", Paul Holland raised one of the most fundamental questions in statistics: What can a statistical model say about causation? This led to his ...
23
votes
5answers
2k views

Introduction to causal analysis

What are good books that introduce causal analysis? I'm thinking of an introduction that both explains the principles of causal analysis and shows how different statistical methods could be used to ...
8
votes
4answers
1k views

Mathematical definition of causality

Let $Y$ and $X$ be random variables. $E(Y|X)$ is the conditional mean of $Y$ given $X$. We say $Y$ is not causally related to $X$ if $E(Y|X)$ does not depend on $X$, which implies it is equal to $E(Y)$...
21
votes
2answers
2k views

What are the main differences between Granger's and Pearl's causality frameworks?

Recently, I ran across several papers and online resources that mention Granger causality. Brief browsing through the corresponding Wikipedia article left me with the impression that this term refers ...
9
votes
2answers
4k views

Unconfoundedness in Rubin's Causal Model- Layman's explanation

When implementing Rubin's causal model, one of the (untestable) assumptions that we need is unconfoundedness, which means $$(Y(0),Y(1))\perp T|X$$ Where the LHS are the counterfactuals, the T is the ...
14
votes
1answer
1k views

Confounder - definition

According to M. Katz in his book Multivariable analysis (Section 1.2, page 6), "A confounder is associated with the risk factor and causally related to the outcome." Why must the confounder be ...
16
votes
3answers
703 views

How is causation defined mathematically?

What is the mathematical definition of a causal relationship between two random variables? Given a sample from the joint distribution of two random variables $X$ and $Y$, when would we say $X$ causes ...
10
votes
3answers
1k views

Is the linearity assumption in linear regression merely a definition of $\epsilon$?

I am revising linear regression. The textbook by Greene states: Now, of course there will be other assumptions on the linear regression model, such as $E(\epsilon|X)=0$. This assumption ...
5
votes
2answers
482 views

Simulating data - correlation vs causation

I'm just dipping my toe into correlation vs causation, so forgive me if I butcher some of the concepts here. To get a better understanding of these concepts from data, I would like to simulate data ...
4
votes
2answers
248 views

T-consistency vs. P-consistency

Francis Diebold has a blog post "Causality and T-Consistency vs. Correlation and P-Consistency" where he presents the notion of P-consistency, or presistency: Consider a standard linear regression ...