DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...

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Causal inference and Bayesian network learning are essential areas of research that focus on understanding and modeling the relationships between variables in various fields, including medicine ...

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Causal inference is the task of drawing conclusions from data about the effects of treatments and other type of interventions. In epidemiology and clinical research, as well as in many other fields, ...

At the core of causal inference lies the challenge of determining reliable causal graphs solely based on observational data. Since the well-known backdoor criterion depends on the graph, any errors in ...