PyWhy Causality in Practice talk series: Keith Battocchi on EconML library and what's new in v0.15

EconML is a Python package that implements several cutting-edge causal inference estimators on top of flexible machine learning methods. In this talk, Keith Battocchi, software engineer at Microsoft Research New England and lead developer for EconML, presents a brief overview of EconML followed by a closer look at several big new features in EconML 0.15. https://github.com/py-why/econml v0.15 brings the following new features: **Model selection over nuisance models**: A much more efficient way of selecting first-stage nuisance models. **Support for federating linear models**: For use cases where models need to be trained on subsets of the data, either because there is more data than can fit on a single machine or for policy reasons, support for aggregating estimates that were generated on disjoint subsets of the data for estimators that use linear regression in their final models, such as LinearDML **Tools for validating CATE estimates**: for CATE estimators with discrete treatments, there is support for a number of new validation methods, including BLP, calibration, and QINI coefficients **PyWhy Causality in Practice**: A talk series focusing on causality and machine learning, especially from a practical perspective. We'll have tutorials and presentations about PyWhy libraries but also talks by external speakers working on causal inference. https://www.pywhy.org/community/videos.html