d-separation in causal inference

written by Eric J. Ma on 2018-08-06 | tags: causal inference bayesian data science

Yesterday evening, I had an empty block of time during which I finally did a worked example of finding whether two nodes are "d-separated" in a causal graph. It was pretty instructive to implement the algorithm. It also reminded me yet again: there's this weird thing about me where I need programming to learn math!

Anyways, if you're interested in seeing the implementation, it's available at GitHub.

Cite this blog post:

@article{
    ericmjl-2018-d-separation-in-causal-inference,
    author = {Eric J. Ma},
    title = {d-separation in causal inference},
    year = {2018},
    month = {08},
    day = {06},
    howpublished = {\url{https://ericmjl.github.io}},
    journal = {Eric J. Ma's Blog},
    url = {https://ericmjl.github.io/blog/2018/8/6/d-separation-in-causal-inference},
}

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