613: Causal Machine Learning

<p>Dr. Emre Kiciman, Senior Principal Researcher at Microsoft Research joins the podcast to share his world-leading knowledge on causal machine learning.<br/><br/>This episode is brought to you by Datalore (<a href='https://datalore.online/SDS'>datalore.online/SDS</a>), the collaborative data science platform, and by Zencastr (<a href='http://zen.ai/sds'>zen.ai/sds</a>), the easiest way to make high-quality podcasts. Interested in sponsoring a SuperDataScience Podcast episode? Visit <a href='https://www.jonkrohn.com/podcast'>JonKrohn.com/podcast</a> for sponsorship information.</p><p>In this episode you will learn:<br/>• What is causal machine learning? [5:52]<br/>• Causal machine learning vs correlational machine learning [10:10]<br/>• Emre’s DoWhy open-source library [16:17]<br/>• The four key steps of causal inference [21:24]<br/>• How and why Emre’s key steps of causal inference will impact ML [26:36]<br/>• Emre&apos;s thoughts on the future of causal inference and AGI [34:09]<br/>• How Emre leverages social media data to solve social problems [38:36]<br/>• What&apos;s next for Emre&apos;s research [46:02]<br/>• The software tools Emre highly recommends [55:16]<br/>• What he looks for in the data science researchers he hires [58:45]</p><p><br/>Additional materials: <a href='https://www.superdatascience.com/613'>www.superdatascience.com/613</a></p>