DoWhy evolves to independent PyWhy model to help causal inference grow

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.

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 evaluation of many

 

 

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