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Works309 from public data
- Review of Causal Discovery Methods Based on Graphical Models1,219
This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three decades, including constraint-based and score-based methods and those based on functional causal models, supplemented by some illustrations and applications.
- Inferring causation from time series in Earth system sciences1,074
An overview of causal inference frameworks is given, promising applications and methodological challenges are identified, and a causality benchmark platform is initiated to close the gap between method users and developers.
- Deep Domain Generalization via Conditional Invariant Adversarial Networks848
This work proposes an end-to-end conditional invariant deep domain generalization approach by leveraging deep neural networks for domain-invariant representation learning and proves the effectiveness of the proposed method.
- Domain Adaptation under Target and Conditional Shift717
This work considers domain adaptation under three possible scenarios, kernel embedding of conditional as well as marginal distributions, and proposes to estimate the weights or transformations by reweighting or transforming training data to reproduce the covariate distribution on the test domain.
- On causal and anticausal learning699
The problem of function estimation in the case where an underlying causal model can be inferred is considered, and a hypothesis for when semi-supervised learning can help is formulated, and corroborate it with empirical results.
- On learning invariant representations for domain adaptation576
This paper constructs a simple counterexample showing that, contrary to common belief, the above conditions are not sufficient to guarantee successful domain adaptation, and proposes a natural and interpretable generalization upper bound that explicitly takes into account the aforementioned shift.
- Causal discovery and inference: concepts and recent methodological advances411
The constraint-based approach to causal discovery, which relies on the conditional independence relationships in the data, is presented, and the assumptions underlying its validity are discussed.
- SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model395
This work proposes a new diffusion-based model named SmartBrush for completing a missing region with an object using both text and shape-guidance and shows that this model outperforms all baselines in terms of visual quality, mask controllability, and background preservation.
- Domain Adaptation with Conditional Transferable Components.374
This paper aims to extract conditional transferable components whose conditional distribution P(T(X)∣Y) is invariant after proper location-scale (LS) transformations, and identifies how P(Y) changes between domains simultaneously.
- 346
- Information-geometric approach to inferring causal directions323
This work defines independence via orthogonality in information space so that it can explicitly describe the kind of dependence that occurs between P"Y and P"X"|"Y making the causal hypothesis ''Y causes X'' implausible.
- Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping272
A geometry-consistent generative adversarial network (Gc-GAN) is developed, which enables one-sided unsupervised domain mapping and reduces the space of possible solutions while keep the correct solutions in the search space.
- 229
- Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery228
Two relaxations are devised called the Randomized Conditional Independence Test (RCIT) and the RCoT which both approximate KCIT by utilizing random Fourier features and return accurate p-values much faster than KCIT in the large sample size context.
- Generalized Score Functions for Causal Discovery212
This paper introduces generalized score functions for causal discovery based on the characterization of general (conditional) independence relationships between random variables, without assuming particular model classes.
- Multi-Source Domain Adaptation: A Causal View212
This paper uses causal models to represent the relationship between the features X and class label Y, and considers possible situations where different modules of the causal model change with the domain, to give an intuitive interpretation of the assumptions underlying certain previous methods.
- Causal-learn: Causal Discovery in Python183
This library focuses on bringing a comprehensive collection of causal discovery methods to both practitioners and researchers, and provides easy-to-use APIs for non-specialists, modular building blocks for developers, detailed documentation for learners, and comprehensive methods for all.
- DeepTrader: A Deep Reinforcement Learning Approach for Risk-Return Balanced Portfolio Management with Market Conditions Embedding182
This paper proposes DeepTrader, a deep RL method to optimize the investment policy, which embeds macro market conditions as an indicator to dynamically adjust the proportion between long and short funds, to lower the risk of market fluctuations, with the negative maximum drawdown as the reward function.
- Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination163
This paper proposes an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover the skeleton of the causal structure over observed variables, and presents a way to determine causal orientations by making use of independence changes in the data distribution implied by the underlying causal model.
- Learning Disentangled Semantic Representation for Domain Adaptation160
This work aims to extract the domain invariant semantic information in the latent disentangled semantic representation (DSR) of the data and devise a dual adversarial network to disentangle these two sets of reconstructed latent variables.
- Probabilistic latent variable models for distinguishing between cause and effect143
A novel method for inferring whether X causes Y or vice versa from joint observations of X and Y is proposed, which considers the hypothetical effect variable to be a function of the hypothetical cause variable and an independent noise term.
- 128
- On Estimation of Functional Causal Models110
This article shows that for any acyclic functional causal model, minimizing the mutual information between the hypothetical cause and the noise term is equivalent to maximizing the data likelihood with a flexible model for the distribution of the noiseTerm, and proposes a Bayesian nonparametric approach based on mutual information minimization.
- 105
- Discovering Temporal Causal Relations from Subsampled Data100
This paper proposes an Expectation-Maximization approach and a variational inference approach to recover temporal causal relations from such subsampled data and proves that if the noise terms are non-Gaussian, the underlying model for the highfrequency data is identifiable from subsampling data under mild conditions.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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