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

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

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.

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.

We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference in differences and synthetic control ...

1 июл. 2024 г. · In SDID, we combine elements of SCM and DID to estimate the treatment effect. Using SCM, we create a synthetic control group.

This sample notebook aims to show readers how to use SynapseML's DiffInDiffEstimator, SyntheticControlEstimator and SyntheticDiffInDiffEstimator to estimate ...

25 апр. 2023 г. · SynthDiD is a generalized version of SCM and DiD that combines the strengths of both methods. It enables causal inference with large panels, even with a short ...

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.