Causality & Big Data – Art of doing causality research with large data sets

The availability of large amounts of transactional and historical data has made it possible to make accurate predictions in many real-world contexts. However, predictive models still don’t help us understand what causes a certain outcome. How can we exploit large datasets to answer deeper questions on causality? What fresh insights can we generate for ML researchers?


The talk would introduce the audience to the art of doing causality research in experimental as well as real-world contexts. We would discuss the science behind real-world randomization examples, such as A/B tests, focusing on how such tests form the holy grail of causal deductions. We would also talk extensively about contexts where randomization is impossible or impractical, and the various statistical techniques (e.g. quasi-experimental methods) that can be used to make causal inferences in such situations.

Who should Attend?

This talk is meant for professionals working in the data science industry and research enthusiasts looking to address causality related questions on using observational data. Also ideal for social scientists (e.g. sociologists, political scientists, economists, etc.) who wish to leverage large real-world datasets to answer questions about human and social behaviour.

Note: No prerequisites. Although, a basic understanding of machine learning and statistical/probability theory would be desirable.

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