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An open source and end-to-end library for causal inference
Manage episode 481575865 series 2570898
This week’s guests are Amit Sharma (Principal Researcher) and Emre Kiciman (Senior Principal Researcher) of Microsoft Research. We talk about practical applications of causal inference, a set of tools and techniques that enable data teams to draw causal conclusions based on data. Amit and Emre are part of the team behind DoWhy, a new open source library for estimating causal effects based on historical data alone, particularly useful when we cannot run an experiment because of time, expense, or ethical concerns.
Download the FREE Report: Trends in Data, Machine Learning, and AI → https://gradientflow.com/2022trendsreport?utm_source=DEpodcast
Subscribe: Apple • Android • Spotify • Stitcher • Google • AntennaPod • RSS.
Detailed show notes can be found on The Data Exchange web site.
321 פרקים
Manage episode 481575865 series 2570898
This week’s guests are Amit Sharma (Principal Researcher) and Emre Kiciman (Senior Principal Researcher) of Microsoft Research. We talk about practical applications of causal inference, a set of tools and techniques that enable data teams to draw causal conclusions based on data. Amit and Emre are part of the team behind DoWhy, a new open source library for estimating causal effects based on historical data alone, particularly useful when we cannot run an experiment because of time, expense, or ethical concerns.
Download the FREE Report: Trends in Data, Machine Learning, and AI → https://gradientflow.com/2022trendsreport?utm_source=DEpodcast
Subscribe: Apple • Android • Spotify • Stitcher • Google • AntennaPod • RSS.
Detailed show notes can be found on The Data Exchange web site.
321 פרקים
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