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BEGIN:VEVENT
UID:eu7itt9mmoq44eblurhv2a0g8s@google.com
CATEGORIES:RTportal.ru
CREATED:20170606T092329
SUMMARY:Коллоквиум ФКН: Causal inference and Kolmogorov complexity
LOCATION:Кочновский проезд, д. 3, лекционный зал Декарт, 3 этаж.
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:t is often stated that “Correlation does not imply causation”: a dependency
  between observed values of two random variables might not imply that there
  exists a causal connection between the corresponding processes (and assumi
 ng there exists one, one might not know the direction). In Shannon informat
 ion theory, this is reflected by the law of “symmetry of information”: I(X;
 Y) = I(Y;X). The information that a random variable X has about Y equals th
 e information that Y has about X.\n\nThis law remains valid if Shannon entr
 opy is replaced by Kolmogorov complexity. However, there exists a subtler s
 etting where this law is violated and one might speculate that this asymmet
 ry can be used to reconstruct causality.\n\nIn the second part of the talk,
  we discuss the postulate of independence of conditionals for the inference
  of causal relations in observed data. This postulate was introduced by D. 
 Janzing and B. Schölkopf in 2010, and the two-variable case states that, if
  X causes Y, then the marginal distribution P X  has low information about 
 the conditional distribution P {Y|X}  (as defined with Kolmogorov complexit
 y). We explain how the most popular methods can be explained by this postul
 ate and overview new methods that were inspired by it. This event was impor
 ted from: https://rtportal.ru/index.php/kalendar-sobytij/eventdetail/166/-/
 kollokvium-fkn-causal-inference-and-kolmogorov-complexity?tmpl=component
DTSTAMP:20260812T210327Z
DTSTART;TZID=Europe/Moscow:20160510T181000
DTEND;TZID=Europe/Moscow:20160510T194000
SEQUENCE:0
TRANSP:OPAQUE
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