This is the Windows app named EconML whose latest release can be downloaded as v0.16.0sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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EconML
DESCRIPTION
EconML is a Python package for estimating heterogeneous treatment effects from observational data via machine learning. This package was designed and built as part of the ALICE project at Microsoft Research with the goal of combining state-of-the-art machine learning techniques with econometrics to bring automation to complex causal inference problems. One of the biggest promises of machine learning is to automate decision-making in a multitude of domains. At the core of many data-driven personalized decision scenarios is the estimation of heterogeneous treatment effects: what is the causal effect of an intervention on an outcome of interest for a sample with a particular set of features? In a nutshell, this toolkit is designed to measure the causal effect of some treatment variable(s) T on an outcome variable Y, controlling for a set of features X, W and how does that effect vary as a function of X.
Features
- Implement recent techniques in the literature at the intersection of econometrics and machine learning
- Documentation available
- Maintain flexibility in modeling the effect heterogeneity
- Use a unified API
- Build on standard Python packages for Machine Learning and Data Analysis
- Examples included
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/econml.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
