A declarative framework for designing efficient causal inference algorithms based on reductions to graph reachability specified by rule tables. For all information on CIfly, see our website.

CIfly can be installed via pip in Python

# Python installation with pip
pip install ciflypy

and from CRAN in R.

# R installation via CRAN
install.packages("ciflyr")

In Python, the installation should work out-of-the-box without relying on any further dependencies. In R, if the package is build on your system, as is the case for Linux distributions, the Rust toolchain needs to be installed.

If you use CIfly in your scientific work, please cite this paper introducing CIfly and its theoretical foundations:

@article{cifly2025,
  author  = {Marcel Wien{"{o}}bst and Sebastian Weichwald and Leonard Henckel},
  title   = {{Linear-Time Primitives for Algorithm Development in Graphical Causal Inference}},
  journal = {{arXiv preprint arXiv:2506.15758}},
  year    = {2025}
}

As an introductory example, we show how to test d-separation with CIfly. The CIfly algorithm specified by the following rule table (saved in the file dsep.txt) returns all nodes d-connected to set X given set Z.

EDGES --> <--
SETS X, Z
START <-- AT X
OUTPUT ...

--> | <-- | current in Z
... | ... | current not in Z

The rule table can be embedded into the code as a multi-line string or loaded via file path such as in the implementation below that tests d-separation using Python

import ciflypy as cf

dsep_table_path = "./dsep.txt"

def test_dsep(G, x, y, Z):
      R = cf.reach(G, {"X": x, "Z": Z}, dsep_table_path)
      return y not in R

# for graph 0 -> 1 -> 2, test whether 0 is d-separated from 2 by 1
print(test_dsep({"-->": [(0, 1), (1, 2)]}, 0, 2, [1]))

and R.

library("ciflyr")

dsepTablePath <- "./dsep.txt"

test_dsep <- function(G, x, y, Z) {
      R <- reach(G, list("X" = x, "Z" = Z), dsepTablePath)
      return (!(y %in% R))
}
# for graph 1 -> 2 -> 3, test whether 1 is d-separated from 3 by 2
print(test_dsep(list("-->" = rbind(c(1, 2), c(2, 3))), 1, 3, c(2)))

Find more details on this example and how to use CIfly in our documentation.

Currently, CIfly is available in Python and R. Both ciflypy and ciflyr are wrappers for the cifly Rust library that implements the rule-table parser and the reachability algorithm. Thus, packages for other languages can be based on the same Rust core. If you are interested in support for a particular language or want to contribute a wrapper package, please open an issue or contact us directly.