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SUMMARY:Bipartite graphical causal models - Joris Mooij (Universiteit van 
 Amsterdam)
DTSTART:20260122T110000Z
DTEND:20260122T114500Z
UID:TALK241822@talks.cam.ac.uk
DESCRIPTION:Based on the popularity of causal Bayesian networks and struct
 ural causal models\, one might expect that these representations are appro
 priate to describe the causal semantics of any real-world system---at leas
 t in principle.\nIn this talk\, I will argue that this is not the case\, a
 nd motivate the study of more general causal modeling frameworks. In parti
 cular\, I will discuss bipartite graphical causal models.\nReal-world comp
 lex systems are often modelled by systems of equations with endogenous and
  independent exogenous random variables. Such models have a long tradition
  in physics and engineering. The structure of such systems of equations ca
 n be encoded by a bipartite graph\, with variable and equation nodes that 
 are adjacent if a variable appears in an equation. I will show how one can
  use Simon&rsquo\;s causal ordering algorithm to derive a Markov property 
 that states the conditional independence for (distributions of) solutions 
 of the equations in terms of the bipartite graph. I will then show how thi
 s Markov property gives rise to a do-calculus for bipartite graphical caus
 al models\, providing these with a refined causal interpretation.
LOCATION:Seminar Room 1\, Newton Institute
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