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Identifying Causal Structure in Your Data
- Helpful if you want to leverage the "Back-Door Adjustment" in order to robustly quantify the effects a variable X has on another Z
- This thread will be highlighting 2 great posts on #causality by @akelleh
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- Helpful if you want to leverage the "Back-Door Adjustment" in order to robustly quantify the effects a variable X has on another Z
- This thread will be highlighting 2 great posts on #causality by @akelleh
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Before jumping into the technical tools, I want to callout that they aren't perfect.
And when developing a causal graph I'd encourage you to use them to help you, not do the job for you.
Be sure to leverage your intuitions and the intuitions of SMEs in addition to this tool.
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And when developing a causal graph I'd encourage you to use them to help you, not do the job for you.
Be sure to leverage your intuitions and the intuitions of SMEs in addition to this tool.
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Causal Graph Inference by @akelleh
- Will be the first post we dive into
- Which explains how to use the IC* algorithm in practice
- Then we'll discuss the theory of the IC* algorithm
https://medium.com/@akelleh/causal-graph-inference-b3e3afd47110
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- Will be the first post we dive into
- Which explains how to use the IC* algorithm in practice
- Then we'll discuss the theory of the IC* algorithm
https://medium.com/@akelleh/causal-graph-inference-b3e3afd47110
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Say we have these 2 graphs and we want to understand the genuine causal relationships between these variables.
- Notice on the left X4 => X5 and on the right X4 and X5 will be correlated but there is no causality there
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- Notice on the left X4 => X5 and on the right X4 and X5 will be correlated but there is no causality there
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First we'll build the left graph and evaluate it.
- Inspecting results: it did a decent job: finding 3/5 of relations, no incorrect ones, and verified one is a genuinely causal (X4->X5)
- We use the IC* algorithm from @akelleh's causality package here: https://github.com/akelleh/causality
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- Inspecting results: it did a decent job: finding 3/5 of relations, no incorrect ones, and verified one is a genuinely causal (X4->X5)
- We use the IC* algorithm from @akelleh's causality package here: https://github.com/akelleh/causality
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Now the right graph
- "[Algorithm] found that X4 and X5 are still correlated in a way that can’t be explained away by the data, but no longer can establish genuine causation."
- "pretty good, considering that there’s a latent confounding variable between X4 and X5 (X6)!"
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![Now the right graph - "[Algorithm] found that X4 and X5 are still correlated in a way that can’t be explained away by the data, but no longer can establish genuine causation."- "pretty good, considering that there’s a latent confounding variable between X4 and X5 (X6)!"/6](https://pbs.twimg.com/media/EjbkFX8XcAExdwq.jpg "Now the right graph - \"[Algorithm] found that X4 and X5 are still correlated in a way that can’t be explained away by the data, but no longer can establish genuine causation.\"- \"pretty good, considering that there’s a latent confounding variable between X4 and X5 (X6)!\"/6")
![Now the right graph - "[Algorithm] found that X4 and X5 are still correlated in a way that can’t be explained away by the data, but no longer can establish genuine causation."- "pretty good, considering that there’s a latent confounding variable between X4 and X5 (X6)!"/6](https://pbs.twimg.com/media/EjbkJkOWoAcAvjL.jpg "Now the right graph - \"[Algorithm] found that X4 and X5 are still correlated in a way that can’t be explained away by the data, but no longer can establish genuine causation.\"- \"pretty good, considering that there’s a latent confounding variable between X4 and X5 (X6)!\"/6")
- "[Algorithm] found that X4 and X5 are still correlated in a way that can’t be explained away by the data, but no longer can establish genuine causation."
- "pretty good, considering that there’s a latent confounding variable between X4 and X5 (X6)!"
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IC* Algorithm
Now I'll be going into weeds a little more on the IC* Algorithm.
IC* is preferred over IC when you have latent variables (don't have all the variables)
Based on 2.6 of "Causality" by Judea Pearl
(Which is "the book" on causality)
http://bayes.cs.ucla.edu/BOOK-2K/book-toc.html
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Now I'll be going into weeds a little more on the IC* Algorithm.
IC* is preferred over IC when you have latent variables (don't have all the variables)
Based on 2.6 of "Causality" by Judea Pearl
(Which is "the book" on causality)
http://bayes.cs.ucla.edu/BOOK-2K/book-toc.html
/6
IC* Algorithm (Step 1)
For every pair of variables (a,b)
- If you can find a set of variables Sab that make a and b independent when you condition on them
- Then don't add an edge
- Else do
Bc if you can do this there must not be a genuine connection between these 2.
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For every pair of variables (a,b)
- If you can find a set of variables Sab that make a and b independent when you condition on them
- Then don't add an edge
- Else do
Bc if you can do this there must not be a genuine connection between these 2.
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IC* Algorithm (Step 2)
For every a,b without an edge, but have a common neighbour c:
- if c is in Sab (is in a path between them)
- then do nothing
- otherwise add arrows: a -> c <- b
Bc if c is not in the path between them, this is only way it could be their neighbour
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For every a,b without an edge, but have a common neighbour c:
- if c is in Sab (is in a path between them)
- then do nothing
- otherwise add arrows: a -> c <- b
Bc if c is not in the path between them, this is only way it could be their neighbour
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IC* Algorithm (Step 3)
For the resulting graph add (recursively) as many edge directions and significance as possible based on the rules:
- R1: if (a ->* c -> b) or (a ->* c - b) then (a ->* c ->* b)
- R2: if (a -> * ... ->* b) then (a -> b)
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For the resulting graph add (recursively) as many edge directions and significance as possible based on the rules:
- R1: if (a ->* c -> b) or (a ->* c - b) then (a ->* c ->* b)
- R2: if (a -> * ... ->* b) then (a -> b)
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@akelleh would also love your thoughts on whether I'm missing anything critical in describing IC*?
Also looking forward to your post on IC*!
Also looking forward to your post on IC*!
