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Found 51 papers, 9 papers with code
Analytic DAG Constraints for Differentiable DAG Learning
By developing the necessary theory to establish a connection between analytic functions and DAG constraints, we demonstrate that analytic functions from the set $\{f(x) = c_0 + \sum_{i=1}^{\infty}c_ix^i | \forall i > 0, c_i > 0; r = \lim_{i\rightarrow \infty}c_{i}/c_{i+1} > 0\}$ can be employed to formulate effective DAG constraints.
I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data?
The remarkable achievements of large language models (LLMs) have led many to conclude that they exhibit a form of intelligence.
Identification of Nonparametric Dynamic Causal Structure and Latent Process in Climate System
The study of learning causal structure with latent variables has advanced the understanding of the world by uncovering causal relationships and latent factors, e. g., Causal Representation Learning (CRL).
Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
Gene Regulatory Network Inference (GRNI) aims to identify causal relationships among genes using gene expression data, providing insights into regulatory mechanisms.
Differentiable Causal Discovery For Latent Hierarchical Causal Models
To the best of our knowledge, this is the first work to propose a differentiable causal discovery method for nonlinear latent hierarchical models.
Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models
We address the issue of the testability of instrumental variables derived from observational data.
Identifiability Analysis of Linear ODE Systems with Hidden Confounders
Subsequently, we extend this analysis to encompass scenarios where hidden confounders exhibit causal dependencies, with the causal structure of latent variables described by a Directed Acyclic Graph (DAG).
Revisiting Differentiable Structure Learning: Inconsistency of $\ell_1$ Penalty and Beyond
Recent advances in differentiable structure learning have framed the combinatorial problem of learning directed acyclic graphs as a continuous optimization problem.
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery
Real-world data often violates the equal-variance assumption (homoscedasticity), making it essential to account for heteroscedastic noise in causal discovery.
Reinforcement Learning for Causal Discovery without Acyclicity Constraints
Recently, reinforcement learning (RL) has proved a promising alternative for conventional local heuristics in score-based approaches to learning directed acyclic causal graphs (DAGs) from observational data.
Rethinking State Disentanglement in Causal Reinforcement Learning
We revisit this research line and find that incorporating RL-specific context can reduce unnecessary assumptions in previous identifiability analyses for latent states.
An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series
Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias.
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations
General intelligence requires quick adaption across tasks.
Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge
We demonstrate that causal exploration aids in learning accurate world models using fewer data and provide theoretical guarantees for its convergence.
On the Parameter Identifiability of Partially Observed Linear Causal Models
Linear causal models are important tools for modeling causal dependencies and yet in practice, only a subset of the variables can be observed.
Optimal Kernel Choice for Score Function-based Causal Discovery
In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data.
Learning Discrete Concepts in Latent Hierarchical Models
Learning concepts from natural high-dimensional data (e. g., images) holds potential in building human-aligned and interpretable machine learning models.
Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning
Recent studies indicate that large multimodal models (LMMs) potentially act as general-purpose assistants and are highly robust against different distributions.
Identifiable Latent Neural Causal Models
This work establishes a {sufficient} and {necessary} condition characterizing the types of distribution shifts for identifiability in the context of latent additive noise models.
Federated Causal Discovery from Heterogeneous Data
This discrepancy has motivated the development of federated causal discovery (FCD) approaches.
Revealing Multimodal Contrastive Representation Learning through Latent Partial Causal Models
Multimodal contrastive representation learning methods have proven successful across a range of domains, partly due to their ability to generate meaningful shared representations of complex phenomena.
Natural Counterfactuals With Necessary Backtracking
Counterfactual reasoning is pivotal in human cognition and especially important for providing explanations and making decisions.
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization
Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features.
A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems.
MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment
Offline Multi-agent Reinforcement Learning (MARL) is valuable in scenarios where online interaction is impractical or risky.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Generator Identification for Linear SDEs with Additive and Multiplicative Noise
In this paper, we present conditions for identifying the generator of a linear stochastic differential equation (SDE) from the distribution of its solution process with a given fixed initial state.
Identifiable Latent Polynomial Causal Models Through the Lens of Change
However, this progress rests on the assumption that the causal relationships among latent causal variables adhere strictly to linear Gaussian models.
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
To this end, we propose a Generalized Independent Noise (GIN) condition for linear non-Gaussian acyclic causal models that incorporate latent variables, which establishes the independence between a linear combination of certain measured variables and some other measured variables.
Causal-learn: Causal Discovery in Python
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering.
Advancing Counterfactual Inference through Nonlinear Quantile Regression
Traditional counterfactual inference, under Pearls' counterfactual framework, typically depends on having access to or estimating a structural causal model.
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
While the majority of current approaches construct the reward redistribution in an uninterpretable manner, we propose to explicitly model the contributions of state and action from a causal perspective, resulting in an interpretable reward redistribution and preserving policy invariance.
Structure Learning with Continuous Optimization: A Sober Look and Beyond
This paper investigates in which cases continuous optimization for directed acyclic graph (DAG) structure learning can and cannot perform well and why this happens, and suggests possible directions to make the search procedure more reliable.
Emerging Synergies in Causality and Deep Generative Models: A Survey
In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance.
Latent Hierarchical Causal Structure Discovery with Rank Constraints
Most causal discovery procedures assume that there are no latent confounders in the system, which is often violated in real-world problems.
Identifiable Latent Causal Content for Domain Adaptation under Latent Covariate Shift
Within this new paradigm, we present an intricate causal generative model by introducing latent noises across domains, along with a latent content variable and a latent style variable to achieve more nuanced rendering of observational data.
Identifying Weight-Variant Latent Causal Models
The task of causal representation learning aims to uncover latent higher-level causal representations that affect lower-level observations.
Factored Adaptation for Non-Stationary Reinforcement Learning
Dealing with non-stationarity in environments (e. g., in the transition dynamics) and objectives (e. g., in the reward functions) is a challenging problem that is crucial in real-world applications of reinforcement learning (RL).
Action-Sufficient State Representation Learning for Control with Structural Constraints
Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks.
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
We show that by explicitly leveraging this compact representation to encode changes, we can efficiently adapt the policy to the target domain, in which only a few samples are needed and further policy optimization is avoided.
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
The first step of our method uses the FCI procedure, which allows confounders and is able to produce asymptotically correct results.
Score-based Causal Discovery from Heterogeneous Data
Most algorithms in causal discovery consider a single domain with a fixed distribution.
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation
We propose counterfactual RL algorithms to learn both population-level and individual-level policies.
Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs
Despite its success in certain domains, most existing methods focus on causal relations between observed variables, while in many scenarios the observed ones may not be the underlying causal variables (e. g., image pixels), but are generated by latent causal variables or confounders that are causally related.
Domain Adaptation as a Problem of Inference on Graphical Models
Such a graphical model distinguishes between constant and varied modules of the distribution and specifies the properties of the changes across domains, which serves as prior knowledge of the changing modules for the purpose of deriving the posterior of the target variable $Y$ in the target domain.
Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering
The learned SSCM gives the specific causal knowledge for each individual as well as the general trend over the population.
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments.
Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes
In this paper, we develop a framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD), to find causal skeleton and directions and estimate the properties of mechanism changes.
Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data
A substantial body of researches use Pearson's correlation coefficients, mutual information, or partial correlation to investigate the differences in brain connectivities between ASD and typical controls from functional Magnetic Resonance Imaging (fMRI).
Multi-domain Causal Structure Learning in Linear Systems
We study the problem of causal structure learning in linear systems from observational data given in multiple domains, across which the causal coefficients and/or the distribution of the exogenous noises may vary.
Causal Generative Domain Adaptation Networks
For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains.
Discovery and Visualization of Nonstationary Causal Models
Third, we develop a method for visualizing the nonstationarity of causal modules.
