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Estimate causal effects with ML

微软研究院博客

A flowchart showing the DoWhy library process. Input Data and Domain Knowledge are injected into the library, where they go through four steps: Model causal mechanisms; Identify target estimands; Estimate causal effect; and Refute estimate. The process produces the output labelled Causal effect.
微软研究院博客

| Emre Kiciman 和 Amit Sharma

Identifying causal effects is an integral part of scientific inquiry. It helps us understand everything from educational outcomes to the effects of social policies to risk factors for diseases. Questions of cause-and-effect are also critical for the design and data-driven…

Hunt Allcott and Greg Lewis on the Microsoft Research Podcast
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In this episode, Senior Principal Researcher Dr. Hunt Allcott speaks with Microsoft Research New England office mate and Senior Principal Researcher Dr. Greg Lewis. Together, they cover the connection between causal machine learning and economics research, the motivations of buyers…