Questions tagged [causality]

The relationship between cause and effect.

Causality (also referred to as causation, or cause and effect) is an influence by which one event, process, state or object (a cause) contributes to the production of another event, process, state or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause.

(Based on Wikipedia.)

Causality can operate at several different levels. The lowest level, observation, is what traditional statistics measures. The middle level, intervention, involves examining what happens under forcing. The highest level is the counterfactual: what would have happened had something been different from the way it was?

In the New Causal Revolution, there are essentially two frameworks for investigation of causal effect: causal graphs a la Judea Pearl, and the potential outcomes framework of Donald Rubin. Both have their respective strengths and weaknesses.

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How exactly does one “control for other variables”?

Here is the article that motivated this question: Does impatience make us fat? I liked this article, and it nicely demonstrates the concept of “controlling for other variables” (IQ, career, income, age, etc) in order to best isolate the true…
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Does causation imply correlation?

Correlation does not imply causation, as there could be many explanations for the correlation. But does causation imply correlation? Intuitively, I would think that the presence of causation means there is necessarily some correlation. But my…
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Under what conditions does correlation imply causation?

We all know the mantra "correlation does not imply causation" which is drummed into all first year statistics students. There are some nice examples here to illustrate the idea. But sometimes correlation does imply causation. The following example…
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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 argument that statistics is never, ever able to…
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Does no correlation imply no causality?

I know that correlation does not imply causality but does an absence of correlation imply absence of causality?
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Criticism of Pearl's theory of causality

In the year 2000, Judea Pearl published Causality. What controversies surround this work? What are its major criticisms?
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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 motto: NO CAUSATION WITHOUT MANIPULATION which…
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Interview question: If correlation doesn't imply causation, how do you detect causation?

I got this question: If correlation doesn't imply causation, how do you detect causation? in an interview. My answer was: You do some form of A/B testing. The interviewer kept prodding me for another approach but I couldn't think of any, and he…
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What is the difference between prediction and inference?

I'm reading through "An Introduction to Statistical Learning" . In chapter 2, they discuss the reason for estimating a function $f$. 2.1.1 Why Estimate $f$? There are two main reasons we may wish to estimate f : prediction and inference. We discuss…
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How are propensity scores different from adding covariates in a regression, and when are they preferred to the latter?

I admit I'm relatively new to propensity scores and causal analysis. One thing that's not obvious to me as a newcomer is how the "balancing" using propensity scores is mathematically different from what happens when we add covariates in a…
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What do "endogeneity" and "exogeneity" mean substantively?

I understand that the basic definition of endogeneity is that $$ X'\epsilon=0 $$ is not satisfied, but what does this mean in a real world sense? I read the Wikipedia article, with the supply and demand example, trying to make sense of it, but it…
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How do DAGs help to reduce bias in causal inference?

I have read in several places that the use of DAGs can help to reduce bias due to Confounding Differential Selection Mediation Conditioning on a collider I also see the term “backdoor path” a lot. How do we use DAGs to reduce these biases, and…
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Correlation does not imply causation; but what about when one of the variables is time?

I know this question has been asked a billion times, so, after looking online, I am fully convinced that correlation between 2 variables does not imply causation. In one of my stats lectures today, we had a guest lecture from a physicist, on the…
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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 front-door from B to A? Because then we should get…
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Can cross validation be used for causal inference?

In all contexts I am familiar with cross-validation it is solely used with the goal of increasing predictive accuracy. Can the logic of cross validation be extended in estimating the unbiased relationships between variables? While this paper by…
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