Causal Inference with Big Data
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Topic : Computer science; 0
Computer science; Keywords: Big Data, Computing
Description :
Causal inference is the study of quantifying whether a treatment, policy, or an intervention, denoted as A, has a causal effect on an outcome interest, denoted as Y. What distinguishes a causal effect of A on Y from an associative effect of A on Y, say by computing the correlation between A and Y, is that under a causal effect, intervening on the treatment A leads to changes in the outcome Y. Hence, a causal effect is a stronger notion of a relationship between A and Y than an associative effect.
Causal Inference with Big Data to be held in Singapore, Singapore between 29 June 2020 and 10 July 2020. It is organised by Institute for Mathematical Sciences/National University of Singapore. It covers specific areas of Computer science such as 0. Visit the website of the conference for more detailed information or contact the organizer for specific questions.
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