Causal Discovery

69 papers with code • 0 benchmarks • 1 datasets

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Greatest papers with code

DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions

microsoft/dowhy 27 Aug 2021

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

Causal Discovery Toolbox: Uncover causal relationships in Python

FenTechSolutions/CausalDiscoveryToolbox 6 Mar 2019

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

High-recall causal discovery for autocorrelated time series with latent confounders

jakobrunge/tigramite NeurIPS 2020

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.

Causal Discovery Time Series

Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets

jakobrunge/tigramite 7 Mar 2020

We consider causal discovery from time series using conditional independence (CI) based network learning algorithms such as the PC algorithm.

Causal Discovery Time Series

Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information

jakobrunge/tigramite 5 Sep 2017

Combining the local permutation scheme with the kernel tests leads to better calibration, but suffers in power.

Causal Discovery

Ordering-Based Causal Discovery with Reinforcement Learning

huawei-noah/trustworthyAI 14 May 2021

It is a long-standing question to discover causal relations among a set of variables in many empirical sciences.

Causal Discovery Variable Selection

Masked Gradient-Based Causal Structure Learning

huawei-noah/trustworthyAI 18 Oct 2019

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.

Causal Discovery Causal Inference

Learning Sparse Nonparametric DAGs

xunzheng/notears 29 Sep 2019

We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data.

Causal Discovery

Causal Discovery with Reinforcement Learning

huawei-noah/trustworthyAI ICLR 2020

The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity.

Causal Discovery Combinatorial Optimization

DAGs with NO TEARS: Continuous Optimization for Structure Learning

xunzheng/notears NeurIPS 2018

This is achieved by a novel characterization of acyclicity that is not only smooth but also exact.

Causal Discovery