Nathaniel Forde: Uncertainty and Causal Inference in Python with CausalPy @ PyCon Ireland 2024 скачать в хорошем качестве

Nathaniel Forde: Uncertainty and Causal Inference in Python with CausalPy @ PyCon Ireland 2024

Uncertainty and Causal Inference in Python with CausalPy - Nathaniel Forde Room 3 Session 05 There is more to causal inference in industry than A/B testing. Often you will want to assess process change where a control group split is wasteful or unethical. To accommodate these questions we can use quasi-experimental causal inference techniques of the kind implemented in CausalPy. CausalPy ensures that we incorporate uncertainty in the estimates of causal effects using Bayesian methods built on top of PyMC. We will survey some recent additions to the package and highlight the centrality and use of causal inference in solving business problems.