Causal Discovery

197 papers with code • 0 benchmarks • 3 datasets

( Image credit: TCDF )

Libraries

Use these libraries to find Causal Discovery models and implementations

Most implemented papers

Masked Gradient-Based Causal Structure Learning

huawei-noah/trustworthyAI 18 Oct 2019

This paper studies the problem of learning causal structures from observational data.

Autoregressive flow-based causal discovery and inference

piomonti/AffineFlowCausalInf 18 Jul 2020

We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual predictions.

Causal Autoregressive Flows

piomonti/carefl 4 Nov 2020

We exploit the fact that autoregressive flow architectures define an ordering over variables, analogous to a causal ordering, to show that they are well-suited to performing a range of causal inference tasks, ranging from causal discovery to making interventional and counterfactual predictions.

Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game

scriddie/varsortability NeurIPS 2021

Here, we show that marginal variance tends to increase along the causal order for generically sampled additive noise models.

Neural graphical modelling in continuous-time: consistency guarantees and algorithms

vanderschaarlab/mlforhealthlabpub ICLR 2022

In this paper, we consider score-based structure learning for the study of dynamical systems.

DiBS: Differentiable Bayesian Structure Learning

larslorch/dibs NeurIPS 2021

In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation.

Efficient Neural Causal Discovery without Acyclicity Constraints

phlippe/ENCO ICLR 2022

Learning the structure of a causal graphical model using both observational and interventional data is a fundamental problem in many scientific fields.

Learning Temporally Latent Causal Processes from General Temporal Data

weirayao/leap ICLR 2022

Our goal is to find time-delayed latent causal variables and identify their relations from temporal measured variables.

Learning Temporally Causal Latent Processes from General Temporal Data

weirayao/leap 11 Oct 2021

In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified from their nonlinear mixtures.

gCastle: A Python Toolbox for Causal Discovery

huawei-noah/trustworthyAI 30 Nov 2021

$\texttt{gCastle}$ is an end-to-end Python toolbox for causal structure learning.