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UID:www.tcs.tifr.res.in/event/749
DTSTAMP:20230914T125936Z
SUMMARY:An Invitation to Causal Inference
DESCRIPTION:Speaker: Piyush Srivastava\n\nAbstract: \nConsider a system con
 sisting of a binary "stimulus" variable X\, (e.g. whether an individual co
 nsumes tobacco)\, a binary "response" variable Y\, (e.g. whether the indiv
 idual develops a particular disease) and a "hidden" confounding variable U
 \, (e.g. a hypothetical genetic factor that affects the individual's prope
 nsity to both consume tobacco as well as to develop the disease).   Give
 n P\, the observed probability distribution of the pair (X\, Y)\, we ask: 
 what is the causal effect of X on Y? In particular\, can it be determined 
 solely from the knowledge of the "correlation" between X and Y?  In the e
 arly 1990s\, Judea Pearl proposed a formalization of the above question in
  the language of directed graphical models.   Put differently\, Pearl's 
 framework formalized what it might mean to compute the strength of a causa
 l relationship (such as may be measured in a controlled experiment) given 
 only data about correlations among different components on the system.\n\n
 It is intuitively clear that this problem is not always solvable: the exam
 ple considered above is a case in point.  However\, a long line of work b
 y several researchers culminated in 2006 in a complete algorithmic charact
 erization of graphical models in which the problem is solvable. Further\, 
 this characterization also included an algorithmic procedure which takes t
 he observed distribution and outputs the requisite "causal" distribution i
 n those cases where the problem is solvable [Huang and Valtorta\, 2006 and
  Shpitser and Pearl\, 2006].\n\nThis talk will introduce directed graphica
 l models and causal inference problem\, and then give an overview of the s
 olution of Huang-Valtorta and Shpitser-Pearl.  We will then look at some 
 recent progress on analyzing the 'robustness' (or 'condition number') of t
 hese solutions with respect to "noise" or "imperfections" in the descripti
 on of the model or in the measurement of the observed distribution.  Surp
 risingly\, even though causal inference is a statistical problem\, such ro
 bustness questions were only asked (and partly answered) recently\, and se
 veral exciting future directions remain open.  Time permitting\, we will 
 then look at other related notions of causal inference\, such as the notio
 n of directed mutual information in information theory\, and the theory of
  linear structural equations.\n\nDisclaimer: Part of this is joint work wi
 th Leonard J. Schulman (available from http://www.tifr.res.in/~piyush.sriv
 astava/research.html#causal-inference). Some other parts are served with g
 entle dollops of caveats emptor.\n \n
URL:https://www.tcs.tifr.res.in/web/events/749
DTSTART;TZID=Asia/Kolkata:20170127T160000
DTEND;TZID=Asia/Kolkata:20170127T173000
LOCATION:A-201 (STCS Seminar Room)
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