A Julia implementation of Recursive Causal Discovery algorithms. Recursive Causal Discovery (RCD) is an efficient approach for causal discovery (i.e., learning a causal graph from data).

The following plots show the F1 score (computed using true causal graph), and number of conditional independency (CI) tests done.

• Recursive Structure Learning (RSL)
  • Learning v-structures
  • Meek's rules
• MArkov boundary-based Recursive Variable ELimination (MARVEL)
• Latent MARVEL (L-MARVEL)
• Removable Order Learning (ROL)

Requires at least Julia 1.10

julia> ]add RecursiveCausalDiscovery

The package so far has only one algorithm implemented: RSL-D, which can be called using the rsld function. The function takes the data matrix/table and a conditional independence test function as input, and returns the completed partially oriented directed acyclic graph (CPDAG) as output.

In this example, a csv file named data.csv is loaded, and the RSL-D algorithm is used to learn the CPDAG. The conditional independence test is based on the Fisher's Z-test.

using RecursiveCausalDiscovery
using CSV
using Tables

# load data (columns are variables and rows are samples)
data = CSV.read("data.csv", Tables.matrix)

# use a Gaussian conditional independence test
sig_level =  0.01
ci_test = (x, y, cond_vec, data) ->  fisher_z(x, y, cond_vec, data, sig_level)

# learn the oriented causal graph using RSL
cpdag =  rsld(data, ci_test)

See the examples/rsl_example_wo_data.jl for a complete example.

See the examples/rsl_example_wo_data.jl for an example on how to generate a random DAG using the Erdos-Renyi model, generate random Gaussian data from it, and learning the CPDAG using RSL-D.

If you do use this package, please cite our work.

@misc{mokhtarian2024rcd,
      title={Recursive Causal Discovery}, 
      author={Ehsan Mokhtarian and Sepehr Elahi and Sina Akbari and Negar Kiyavash},
      year={2024},
      eprint={2403.09300},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2403.09300}, 
}

Thanks to Felix Wechsler for helping me with Julia and pushing me to implement RCD in Julia.