causality-lab

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

Code for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.

Amortized Inference for Causal Structure Learning, NeurIPS 2022

Active Bayesian Causal Inference (Neurips'22)

Code for the paper "Estimating Transfer Entropy via Copula Entropy"

Code for "LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysis" paper

[TMLR23] FedDAG: Federated DAG Structure Learning