fastEDM

The fastEDM Python package implements a series of Empirical Dynamic Modeling tools that can be used for causal analysis of time series data.

Key features of the package:

• powered by a fast multi-threaded C++ backend,
• able to process panel data, a.k.a. multispatial EDM,
• able to handle missing data using new dt algorithms or by dropping points.

Installation

You can install the development version of fastEDM from GitHub with:

pip install 'fastEDM @ git+https://github.com/EDM-Developers/fastEDM-python'

Example: Chicago crime levels and temperature

This example, looking at the causal links between Chicago’s temperature and crime rates, is described in full in our paper:

from fastEDM import easy_edm
import pandas as pd

url = "https://github.com/EDM-Developers/fastEDM-r/raw/main/vignettes/chicago.csv"
chicago = pd.read_csv(url)

crimeCCMCausesTemp = easy_edm("Crime", "Temperature", data=chicago, verbosity=0)
#> No evidence of CCM causation from Crime to Temperature found.
tempCCMCausesCrime = easy_edm("Temperature", "Crime", data=chicago, verbosity=0)
#> Some evidence of CCM causation from Temperature to Crime found.

Stata & R Packages

This Python package, and the R fastEDM package, is port of our EDM Stata package. As all packages share the same underlying C++ code, their behaviour will be identical. If you plan to adjust some of the various low-level EDM parameters, check out the documentation of the Stata package for more details on their options and behaviours.

Other Resources

This site serves as the primary source of documentation for the package, though there is also:

• our Stata Journal paper which explains the package and the overall causal framework, and
• Jinjing's QMNET seminar on the package, the recording is on YouTube and the slides are here.

Authors

• Jinjing Li (author),
• Michael Zyphur (author),
• Patrick Laub (author, maintainer),
• Edoardo Tescari (contributor),
• Simon Mutch (contributor),
• Rishi Dhushiyandan (contributor),
• George Sugihara (originator)

Citation

Jinjing Li, Michael J. Zyphur, George Sugihara, Patrick J. Laub (2021), Beyond Linearity, Stability, and Equilibrium: The edm Package for Empirical Dynamic Modeling and Convergent Cross Mapping in Stata, Stata Journal, 21(1), pp. 220-258

@article{edm-stata,
  title={Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in {S}tata},
  author={Li, Jinjing and Zyphur, Michael J and Sugihara, George and Laub, Patrick J},
  journal={The Stata Journal},
  volume={21},
  number={1},
  pages={220--258},
  year={2021},
}