See the jupyter notebook for basic usage · In this note, we will check how the estimation results change with changes in the scale of the donor pool features.

This package implements the synthetic difference-in-differences estimation procedure, along with a range of inference and graphing procedures.

Synthetic difference in differences for Python . Contribute to MasaAsami/pysynthdid development by creating an account on GitHub.

This package implements the synthetic difference in difference estimator (SDID) for the average treatment effect in panel data, as proposed in Arkhangelsky ...

This article is a brief introduction to Synthetic Difference in Differences (SDID) in the following paper and a brief description of how to run it in Python.

PySINDy is a sparse regression package with several implementations for the Sparse Identification of Nonlinear Dynamical systems (SINDy) method.

PySynth is a package to create synthetic datasets - that is, datasets that look just like the original in terms of statistical properties, variable values, ...

This new Synthetic Difference-in-Differences estimation procedure manages to exploit advantages of both methods while also increasing the precision.

3 мар. 2022 г. · DID requires the selection of a control group that has parallel trends in the treatment group and the pre-intervention period.

This notebook gives an almost exhaustive overview of the different features available in PySINDy. It's a good reference for how to set various options and work ... Introduction to SINDy · Original Paper: Sparse... · Feature Overview