# Is the use of DAGs in causal inference commonplace?
**URL:** https://discourse.julialang.org/t/is-the-use-of-dags-in-causal-inference-commonplace/108984
**Category:** Statistics
**Tags:** graphs, causal-inference
**Created:** [January 18, 2024, 8:28pm UTC](https://discourse.julialang.org/t/is-the-use-of-dags-in-causal-inference-commonplace/108984 "2024-01-18T20:28:15Z")
**Posts on this page:** 8
**Page:** 1
### Author:  [@mthelm85](https://discourse.julialang.org/u/mthelm85)
#### Post date: [January 18, 2024, 8:28pm UTC](https://discourse.julialang.org/t/is-the-use-of-dags-in-causal-inference-commonplace/108984/1 "2024-01-18T20:28:15Z")
A few years ago I worked through Richard McElreath’s _Statistical Rethinking_. In it, McElreath discusses the use of Directed Acyclic Graphs to specify/analyze causal models, briefly covers do-calculus, etc. I don’t work in academia, but I do occasionally read research related to low wage workers, labor law violations, and related topics. One thing that I almost never see in the publications are DAGs.
I know there are a lot of scientists/academics in this community, so I’m wondering how common it is to use DAGs in causal inference, and also how common it is to actually include the DAGs in the publications themselves.
Also, I recently was discussing an issue with an individual whose title is _Principle Research Scientist_, and this person was unfamiliar with DAGs and their use in causal inference, which surprised me a little. Are DAGs commonly a part of PhD programs, or do they tend to be taught/used only in certain fields?
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### Author:  [@facusapienza](https://discourse.julialang.org/u/facusapienza)
#### Post date: [January 18, 2024, 8:58pm UTC](https://discourse.julialang.org/t/is-the-use-of-dags-in-causal-inference-commonplace/108984/2 "2024-01-18T20:58:39Z")
Hi @mthelm85
The short answer to your question will be _yes_, DAGs are very common in causal inference and many causal inference application use DAGs for experiment design. If you are familiar with the do-calculus, you probably remember than here DAGs is how the dependency between variables is represented.
However, it is true that there are two traditions or two parallel developments in causal inference. The one that strongly relies in DAGs is the one developed by people like Judea Pearl defining DAGs and probabilistic models on top of them. If you are interested in this approach, a seminar piece of work is this [paper](https://www.jstor.org/stable/2337329) and the _Book of Why_ sort of introduces the same framework. On the second hand, there is another _tradition_ (not sure if this is the best word to describe this) that does causal inference without dealing DAGs (or at least no explicitly) and by just handling the probabilistic dependencies between variables. An excellent reference for an introductory view to causal inference are the [Notes of Peng Ding on Causal Inference](https://arxiv.org/pdf/2305.18793.pdf).
So, I think you can always think that there is a DAG in your causal model, but it is common in modern frameworks to use a different jargon, reason why some people doing causal inference may be unfamiliar with DAGs.
Hope this helps clarifying your doubts!
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### Author:  [@jar1](https://discourse.julialang.org/u/jar1)
#### Post date: [January 18, 2024, 9:07pm UTC](https://discourse.julialang.org/t/is-the-use-of-dags-in-causal-inference-commonplace/108984/3 "2024-01-18T21:07:03Z")
Afaict DAGs have only been taught widely within the last decade, so a scientist whose training was earlier may not have trained on them.
As for publications, I think the conventions are still being worked out, so “it depends”. They’re often used in the development of models; the DAG drawing may or may not be actually presented in the text. In epidemiology & public health they’re common, also increasing in social sciences. You can see papers citing Dagitty, a popular R package for DAGs, on [Google Scholar](https://scholar.google.com/scholar?cites=16020027576647875993&as_sdt=2005&sciodt=0,5&hl=en).
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### Author:  [@mthelm85](https://discourse.julialang.org/u/mthelm85)
#### Post date: [January 18, 2024, 9:33pm UTC](https://discourse.julialang.org/t/is-the-use-of-dags-in-causal-inference-commonplace/108984/4 "2024-01-18T21:33:34Z")
> As for publications, I think the conventions are still being worked out, so “it depends”. They’re often used in the development of models; the DAG drawing may or may not be actually presented in the text.
I find it very disappointing that they aren’t more widely included in the papers I read…for me, they are extremely helpful in understanding the structure of the problem and it makes it so much easier to understand the statistical models/data/tables/graphs that are always included.
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### Author:  [@pdeffebach](https://discourse.julialang.org/u/pdeffebach)
#### Post date: [January 18, 2024, 9:55pm UTC](https://discourse.julialang.org/t/is-the-use-of-dags-in-causal-inference-commonplace/108984/5 "2024-01-18T21:55:34Z")
You might be interested in [this](https://www.nber.org/papers/w31945) recent paper by economist James Heckman on DAGs versus other tools for causal inference.
There is also [this](https://www.aeaweb.org/articles?id=10.1257/jel.20191597) review by Guido Imbens
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### Author:  [@nilshg](https://discourse.julialang.org/u/nilshg)
#### Post date: [January 19, 2024, 9:26am UTC](https://discourse.julialang.org/t/is-the-use-of-dags-in-causal-inference-commonplace/108984/6 "2024-01-19T09:26:27Z")
Came here to post the Imbens paper which I think does a great job of summarising the two approaches and discussing why Pearl’s approach hasn’t found as much application as he’d like in the mainstream. Pearl of course takes issue with this, see e.g. here:
[http://causality.cs.ucla.edu/blog/index.php/2020/01/29/on-imbens-comparison-of-two-approaches-to-empirical-economics/](http://causality.cs.ucla.edu/blog/index.php/2020/01/29/on-imbens-comparison-of-two-approaches-to-empirical-economics/)
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### Author:  [@amrods](https://discourse.julialang.org/u/amrods)
#### Post date: [January 21, 2024, 12:13am UTC](https://discourse.julialang.org/t/is-the-use-of-dags-in-causal-inference-commonplace/108984/7 "2024-01-21T00:13:00Z")
Came to mention that paper. There is another one as well: [https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-051520-015456](https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-051520-015456).
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### Author:  [@mschauer](https://discourse.julialang.org/u/mschauer)
#### Post date: [March 10, 2024, 10:45am UTC](https://discourse.julialang.org/t/is-the-use-of-dags-in-causal-inference-commonplace/108984/8 "2024-03-10T10:45:09Z")
A directed graph is just a representation of a list of statements „A has a direct effect on B“, „B has a direct effect on …“. You can just say these things in words, but if you want to shift work from your brain to say [GitHub - mschauer/CausalInference.jl: Causal inference, graphical models and structure learning in Julia](https://github.com/mschauer/CausalInference.jl/) you need a common language. in Julia a good way is encoding this information in a DiGraph object from [GitHub - JuliaGraphs/Graphs.jl: An optimized graphs package for the Julia programming language](https://github.com/JuliaGraphs/Graphs.jl)