This repository contains multiple notebook solution packages (fastDTW, etc.) for Marketing Analysts and Data Scientists to optimize experimental design and causal inference analysis (geo split, causal impact analysis etc.)
Some marketing practitioners pay attention to Causal inference in statistics. However, using time series data without parallel trend assumptions does not allow for appropriate analysis. Therefore, the purpose is to enable the implementation and analysis of interventions after classifying time-series data for which parallel trend assumptions can be made.
For contributions, see CONTRIBUTING.md.