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, but are unified at the Structural Causal Model level.

1812 questions
103
votes
7 answers

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…
January
  • 7,559
75
votes
6 answers

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?
Neil G
  • 15,219
64
votes
3 answers

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…
Shane
  • 12,461
35
votes
4 answers

Difference between rungs two and three in the Ladder of Causation

In Judea Pearl's "Book of Why" he talks about what he calls the Ladder of Causation, which is essentially a hierarchy comprised of different levels of causal reasoning. The lowest is concerned with patterns of association in observed data (e.g.,…
dsaxton
  • 12,138
  • 1
  • 26
  • 48
14
votes
2 answers

Problems in Causality from Judea Pearl Book

I'm starting to read Causal Inference in Statistics, A Primer by Judea Pearl et. al. I have a masters in math, but I have never taken a statistic course. I'm a bit confused by one of the early study questions, and there's no one I can ask about…
saulspatz
  • 308
11
votes
1 answer

is it always "no causation without manipulation"?

I am new to statistics and causality. To my knowledge, to talk about causality, one must have some sort of intervention. I knew it as "no causation without manipulation". Now I am curious: I see many information saying that causality can be checked…
8
votes
1 answer

Causal Inference When Treatment and Outcome Are Aggregated At Different Levels

Consider a scenario where a treatment is a U.S. state-level policy (some states adopted the policy while others did not) and the outcome are individual-level responses to a survey across American states. To make this scenario less abstract, let's…
6
votes
2 answers

Causal Inference When Treatment Assignment is Fully-Known But Not Randomized

Consider a situation where a public formula specifies the amount of treatment (the dollar amount of financial aid) students receive and I am interested in estimating the causal effect of treatment on a given outcome. For example, say that I know…
6
votes
1 answer

Comparison of IPTW and regression adjustment in causal inference

Please see the reproducible R code in the end. The simulated data is from section 4.1 in the paper "ipw: An R Package for Inverse Probability Weighting", where we have measurements in 1000 individuals on a continuous confounder L, a dichotomous…
Statisfun
  • 707
6
votes
2 answers

Potential outcomes selection bias

I have an issue with the derivation of the selection bias from the Rubin potential outcomes framework. I am looking at this slide who shows how to go from a difference in means between treated and controls to the ATT + selection bias. However, I…
giac
  • 901
6
votes
3 answers

Is causal inference only from data possible?

Suppose we are given a dataset but not the capability of performing some AB testing. We do some regression using X as predictor and Y as response and get a model. Can we actually say something about the causal relationship between X and Y? Or is it…
DiveIntoML
  • 2,033
6
votes
2 answers

Understanding Judea Pearl's Back-Door Adjustment Formula

I'm reading Judea Pearl's "Book of Why" and although I find it really interesting (and potentially useful) I find the lack of explicit equations difficult to deal with. I want to know if I'm getting the back-door adjustment formula correct. I…
5
votes
1 answer

What is the difference between the G-formula, G-estmation, G-computation and G-methods

I've seen a similar questions posted but I wasn't sure on the answers that were provided. Similar to when I try and look these methods up, I've seen more general abstract descriptions that were hard to understand. I think I've read that G-method is…
Geoff
  • 601
5
votes
2 answers

Causation implication

I recently read an article about how you can increase longevity by sleeping less. This article, like many others I've read, references a statistical study and implies that causation was found between two events(sleeping less and living longer). Well…
Deiwin
  • 153
4
votes
3 answers

Can causality be inferred in a study with an experience followed by two sets of measures

I came across this study as part of a mock exam paper and was confused to say the least. Context: The study investigates cognitive and behavioural factor related to the experience of anxiety in MRI scanners. Participants completed the following…
user3171
1
2 3 4 5