How Markov assumption links DAG to data

Episode C3VL3: Markov assumption links DAG to data.

In this episode of 𝗖𝗮𝘂𝘀𝗮𝗹𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗕𝗼𝗿𝗶𝘀, we explore how advances in causal inference methods led to a fascinating discovery: connection between a directed acyclic graph and the joint probability for its variables. DAG depicts the dependency of multivariate data. And data present themselves as joint distributions. Main message here it is the Markov assumption that establishes connection between the dependency structure of directed acyclic graphs and data, linking the factorization of joint probability for all variables to their conditional probabilities.

0:00 Intro
0:57 Markov assumption
3:55 Joint distribution
5:39 Exogenous variation
7:49 Associations implied by DAG
10:57 Take-aways

Articles:
➟ Westreich 2013 https://doi.org/10.1093/aje/kws412
➟ Digitale 2022 https://doi.org/10.1016/j.jclinepi.20...

Extras from 𝗖𝗮𝘂𝘀𝗮𝗹𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗕𝗼𝗿𝗶𝘀
⇒ http://tiny.cc/C3VL3


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