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 technological systems we build today.  To help data scientists better understand and … Continue reading DoWhy evolves to independent PyWhy model to help causal inference grow