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InfoQ Homepage Presentations Causal Inference in Data Science

Causal Inference in Data Science

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Summary

Amit Sharma discusses the value of counterfactual reasoning and causal inference, demonstrating that relying on predictive modeling based on correlations can be counterproductive.

Bio

Amit Sharma is a researcher at Microsoft. He has worked on applications that include estimating the impact of recommender systems on purchasing decisions, influence of seeing friends' activities through feeds in social networks, and predicting the popularity of books or music. He is a recipient of the 2012 Yahoo! Key Scientific Challenges Award and 2009 Honda Young Engineer and Scientist Award.

About the conference

Data Science is an emerging field that allows businesses to effectively mine historical data and better understand consumer behavior. This type of scientific data management approach is critical for any business to successfully launch its products and better serve its existing markets.

Recorded at:

Aug 11, 2017

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