EconML
Estimate causal effects with ML
News & features
DoWhy evolves to independent PyWhy model to help causal inference grow
| Emre Kiciman and Amit Sharma
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…
Econ2: Causal machine learning, data interpretability, and online platform markets featuring Hunt Allcott and Greg Lewis
In this episode, Senior Principal Researcher Dr. Hunt Allcott speaks with Microsoft Research New England office mate and Senior Principal Researcher Dr. Greg Lewis. Together, they cover the connection between causal machine learning and economics research, the motivations of buyers…
In the news | GitHub
EconML Code Release v0.7.0
This is a major release, see release notes here.
In the news | Open Data Science Conference
Machine Learning Estimation of Heterogeneouse Treatement Effects: the Microsoft EconML Library Talk
One of the biggest promises of machine learning is the automation of decision making in a multitude of application domains. A core problem that arises in most data-driven personalized decision scenarios is the estimation of heterogeneous treatment effects: what is…
In the news | Counterfactual
Introduction to EconML Packages (meta-learners)
In recent years, research in the fusion of econometrics and machine learning has been booming. For example, related Tutorials are held in KDD2018 and NeurIPS2018. As one of the trends, Microsoft Research has published a package called EconML, and I…