Causal Discovery
State-of-the-art 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.
SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning
We present the Structural Agnostic Model (SAM), a framework to estimate end-to-end non-acyclic causal graphs from observational data.
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.
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.
Discovering Causal Signals in Images
CVPR 2017 • kyrs/NCC-experiments
Our experiments demonstrate the existence of a relation between the direction of causality and the difference between objects and their contexts, and by the same token, the existence of observable signals that reveal the causal dispositions of objects.
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.
A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias.