Causal Discovery
69 papers with code • 0 benchmarks • 1 datasets
( Image credit: TCDF )
Benchmarks
BCOPA-CE
Greatest papers with code
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions
Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and whether all relevant confounders are observed.
Causal Discovery Toolbox: Uncover causal relationships in Python
FenTechSolutions/CausalDiscoveryToolbox
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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.
High-recall causal discovery for autocorrelated time series with latent confounders
We show that existing causal discovery methods such as FCI and variants suffer from low recall in the autocorrelated time series case and identify low effect size of conditional independence tests as the main reason.
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets
We consider causal discovery from time series using conditional independence (CI) based network learning algorithms such as the PC algorithm.
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.
Ordering-Based Causal Discovery with Reinforcement Learning
It is a long-standing question to discover causal relations among a set of variables in many empirical sciences.
Masked Gradient-Based Causal Structure Learning
We next utilize the augmented form to develop a masked structure learning method that can be efficiently trained using gradient-based optimization methods, by leveraging a smooth characterization on acyclicity and the Gumbel-Softmax approach to approximate the binary adjacency matrix.
Learning Sparse Nonparametric DAGs
xunzheng/notears
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We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data.
Causal Discovery with Reinforcement Learning
huawei-noah/trustworthyAI
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ICLR 2020
The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity.
DAGs with NO TEARS: Continuous Optimization for Structure Learning
xunzheng/notears
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NeurIPS 2018
This is achieved by a novel characterization of acyclicity that is not only smooth but also exact.