Causal Discovery
( Image credit: TCDF )
Leaderboards
Greatest papers with code
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
Combining the local permutation scheme with the kernel tests leads to better calibration, but suffers in power.
Causal Discovery Toolbox: Uncover causal relationships in Python
• Diviyan-Kalainathan/CausalDiscoveryToolbox
• 
This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling.
Causal Discovery with Attention-Based Convolutional Neural Networks
Machine Learning and Knowledge Extraction 2019
• M-Nauta/TCDF
•
We therefore present the Temporal Causal Discovery Framework (TCDF), a deep learning framework that learns a causal graph structure by discovering causal relationships in observational time series data.
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper.
Revisiting Classifier Two-Sample Tests
The goal of this paper is to establish the properties, performance, and uses of C2ST.
Ancestral Causal Inference
Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions.
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders
We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, latent confounders and non-linearities.
Algebraic Equivalence of Linear Structural Equation Models
Despite their popularity, many questions about the algebraic constraints imposed by linear structural equation models remain open problems.
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
We show that the data generated from our simulator have similar statistics as real-world data.
Causal Discovery with Cascade Nonlinear Additive Noise Models
In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect.