Intervention in undirected Ising graphs and the partition function

Lourens Waldorp; Maarten Marsman

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Intervention in undirected Ising graphs and the partition function

Lourens Waldorp, Maarten Marsman

Methodology · Submitted 27 May 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim And if the weights vary according to a sub-Gaussian distribution, then the approximation is exponentially close to the correct one.

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What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.57 · human participants 0.17 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.51 · compares with prior results 0.15 · mentions code or data availability 0.03.

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arXiv listing abstract v1. Results and the main claim are underlined; show every role.

Undirected graphical models have many applications in such areas as machine learning, image processing, and, recently, psychology. Psychopathology in particular has received a lot of attention, where symptoms of disorders are assumed to influence each other. One of the most relevant questions practically is on which symptom (node) to intervene to have the most impact. Interventions in undirected graphical models is equal to conditioning, and so we have available the machinery with the Ising model to determine the best strategy to intervene. In order to perform such calculations the partition function is required, which is computationally difficult. Here we use a Curie-Weiss approach to approximate the partition function in applications of interventions. We show that when the connection weights in the graph are equal within each clique then we obtain exactly the correct partition function. And if the weights vary according to a sub-Gaussian distribution, then the approximation is exponentially close to the correct one. We confirm these results with simulations.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Undirected graphical models have many applications in such areas as machine learning, image processing, and, recently, psychology.	background 1.00	0.04	0.02	asserted 1.00
s1	Psychopathology in particular has received a lot of attention, where symptoms of disorders are assumed to influence each other.	background 1.00	0.04	0.02	asserted 0.98
s2	One of the most relevant questions practically is on which symptom (node) to intervene to have the most impact.	background 0.97	0.05	0.04	asserted 0.93
s3	Interventions in undirected graphical models is equal to conditioning, and so we have available the machinery with the Ising model to determine the best strategy to intervene.	background 0.69	0.10	0.08	asserted 1.00
s4	In order to perform such calculations the partition function is required, which is computationally difficult.	background 0.63	0.09	0.02	asserted 1.00
s5	Here we use a Curie-Weiss approach to approximate the partition function in applications of interventions.	method 0.94	0.90	0.14	asserted 0.99
s6	We show that when the connection weights in the graph are equal within each clique then we obtain exactly the correct partition function.	result 1.00	0.91	0.29	asserted 1.00
s7	And if the weights vary according to a sub-Gaussian distribution, then the approximation is exponentially close to the correct one.	result 1.00	0.86	0.25	asserted 1.00
s8	We confirm these results with simulations.	result 0.39	0.95	0.07	asserted 1.00

What kind of paper the classifier reports this is: theoretical result 0.58 · evidence from proof or derivation 0.71.

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Original abstract

Undirected graphical models have many applications in such areas as machine learning, image processing, and, recently, psychology. Psychopathology in particular has received a lot of attention, where symptoms of disorders are assumed to influence each other. One of the most relevant questions practically is on which symptom (node) to intervene to have the most impact. Interventions in undirected graphical models is equal to conditioning, and so we have available the machinery with the Ising model to determine the best strategy to intervene. In order to perform such calculations the partition function is required, which is computationally difficult. Here we use a Curie-Weiss approach to approximate the partition function in applications of interventions. We show that when the connection weights in the graph are equal within each clique then we obtain exactly the correct partition function. And if the weights vary according to a sub-Gaussian distribution, then the approximation is exponentially close to the correct one. We confirm these results with simulations.

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