Methods and tools for causal discovery and causal inference
Sign Up to like & getrecommendations! Published in 2022 at "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"
DOI: 10.1002/widm.1449
Abstract: Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of… read more here.
Keywords: causal inference; methods tools; discovery causal; tools causal ... See more keywords
Can algorithms replace expert knowledge for causal inference? A case study on novice use of causal discovery
Sign Up to like & getrecommendations! Published in 2024 at "American Journal of Epidemiology"
DOI: 10.1093/aje/kwae338
Abstract: Abstract With growing interest in causal inference and machine learning among epidemiologists, there is increasing discussion of causal discovery algorithms for guiding covariate selection. We present a case study of novice application of causal discovery… read more here.
Keywords: causal; case study; causal discovery;
Software application profile: tpc and micd-R packages for causal discovery with incomplete cohort data.
Sign Up to like & getrecommendations! Published in 2024 at "International journal of epidemiology"
DOI: 10.1093/ije/dyae113
Abstract: MOTIVATION The Peter Clark (PC) algorithm is a popular causal discovery method to learn causal graphs in a data-driven way. Until recently, existing PC algorithm implementations in R had important limitations regarding missing values, temporal… read more here.
Keywords: causal discovery; tpc; micd packages; micd ... See more keywords
A Fast PC Algorithm for High Dimensional Causal Discovery with Multi-Core PCs
Sign Up to like & getrecommendations! Published in 2019 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2016.2591526
Abstract: Discovering causal relationships from observational data is a crucial problem and it has applications in many research areas. The PC algorithm is the state-of-the-art constraint based method for causal discovery. However, runtime of the PC… read more here.
Keywords: causal discovery; high dimensional; causal; multi core ... See more keywords
Large-Scale Hierarchical Causal Discovery via Weak Prior Knowledge
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2025.3537832
Abstract: Causal discovery faces significant challenges as the number of hypotheses grows exponentially with the number of variables. This complexity becomes particularly daunting when dealing with large sets of variables. We introduce a novel divide-and-conquer method… read more here.
Keywords: causal discovery; scale hierarchical; large scale; sub sets ... See more keywords
Out-of-Sample Tuning for Causal Discovery.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3185842
Abstract: Causal discovery is continually being enriched with new algorithms for learning causal graphical probabilistic models. Each one of them requires a set of hyperparameters, creating a great number of combinations. Given that the true graph… read more here.
Keywords: tuning causal; causal; oct; sample tuning ... See more keywords
Causal Discovery on Discrete Data via Weighted Normalized Wasserstein Distance.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3213641
Abstract: The task of causal discovery from observational data (X,Y) is defined as the task of deciding whether X causes Y , or Y causes X or if there is no causal relationship between X and… read more here.
Keywords: conditional distributions; weighted normalized; normalized wasserstein; distributions noise ... See more keywords
Higher Order Cumulants-Based Method for Direct and Efficient Causal Discovery.
Sign Up to like & getrecommendations! Published in 2025 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2025.3622148
Abstract: Causal discovery plays a pivotal role in scientific inquiry and subsequent applications in prediction or decision-making. While many methods have been proposed, many of them rely on independence tests. However, these tests are difficult to… read more here.
Keywords: causal; higher order; method; causal discovery ... See more keywords
Individualized causal discovery with latent trajectory embedded bayesian networks.
Sign Up to like & getrecommendations! Published in 2023 at "Biometrics"
DOI: 10.1111/biom.13843
Abstract: Bayesian networks have been widely used to generate causal hypotheses from multivariate data. Despite their popularity, the vast majority of existing causal discovery approaches make the strong assumption of a (partially) homogeneous sampling scheme. However,… read more here.
Keywords: discovery latent; individualized causal; bayesian networks; causal discovery ... See more keywords
Bivariate Causal Discovery and Its Applications to Gene Expression and Imaging Data Analysis
Sign Up to like & getrecommendations! Published in 2018 at "Frontiers in Genetics"
DOI: 10.3389/fgene.2018.00347
Abstract: The mainstream of research in genetics, epigenetics, and imaging data analysis focuses on statistical association or exploring statistical dependence between variables. Despite their significant progresses in genetic research, understanding the etiology and mechanism of complex… read more here.
Keywords: imaging data; bivariate causal; data analysis; analysis ... See more keywords
Scalable Time Series Causal Discovery with Approximate Causal Ordering
Sign Up to like & getrecommendations! Published in 2024 at "Mathematics"
DOI: 10.3390/math13203288
Abstract: Causal discovery in time series data presents a significant computational challenge. Standard algorithms are often prohibitively expensive for datasets with many variables or samples. This study introduces and validates a heuristic approximation of the VarLiNGAM… read more here.
Keywords: time; causal discovery; causal; time series ... See more keywords