Paper tackles confounded ANMs, estimating ACEs with minimal interventions.
problem Estimating causal effects in the presence of unobserved confounders.
method Interventional distributions and randomized algorithm to reduce the number of required interventions.
result Poly-logarithmic number of interventions sufficient to infer causal effects in confounded ANMs.
A new sorting method using R2 values improves causal discovery from noisy data.
problem Improving causal discovery from noisy observational data.
method Introducing R2-sortability and an algorithm, R2-SortnRegress, to find causal order. result Sorting variables by increasing R2 yields a close-to-causal order. LANCA uses ANM to learn latent causal factors without supervision.
problem Learning latent causal factors without supervision.
method LANCA employs a deterministic Wasserstein Auto-Encoder coupled with a differentiable ANM Layer.
result LANCA outperforms baselines on physics and photorealistic environments.
Study evaluates how noise affects ANMs' ability to identify causal directions.
problem Challenges in identifying causal relationships in bivariate cases with noise.
method Empirical study using Regression with Subsequent Independence Test (RESIT) on various ANM models.
result ANMs can fail to identify true causal directions for certain noise levels.
A new method for identifying causal directions in complex systems.
problem Identifying causal relationships in nonlinear systems with limited data.
method Sequential edge orientation approach using pairwise additive noise model.
result The method can recover true causal DAGs under nonlinear additive noise models.
MissDAG addresses causal discovery with missing data using imputation and EM.
problem Causal discovery with missing data in incomplete observational studies.
method MissDAG uses EM framework to maximize likelihood of visible data, leveraging ANMs and Monte Carlo EM for approximations.
result MissDAG outperforms two-step imputation and causal discovery methods.
The inference of the causal relationship between a pair of observed variables is a fundamental problem in science, and most existing approaches are based on one single causal model. In practice, however, observations are often collected from multiple sources with heterogeneous causal models due to certain uncontrollabl…
Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference. Causal inference has the potential to hav…
This study examines how noise levels affect causal discovery methods.
problem Impact of noise levels on causal discovery methods.
method Empirical study using Regression with Subsequent Independence Test and Identification using Conditional Variances on ANMs with varying noise levels.
result Causal discovery methods can fail for certain noise levels.
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. The 'cdt' package implements the end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the ca…
Identifies shifts in causal mechanisms between related datasets using ANMs.
problem Estimating the full causal structure from data is challenging; focus on identifying shifts in causal mechanisms.
method Assumes nonlinear additive noise models, uses Jacobian of score function for mixture distribution to identify shifts.
result Shows applicability of the approach on synthetic and real-world data.
We address the problem of two-variable causal inference without intervention. This task is to infer an existing causal relation between two random variables, i.e. X→Y or Y→X , from purely observational data. As the option to modify a potential cause is not given in many situations only struc…
A new method tests independence for causal discovery on discrete data.
problem Inferring causal directions on discrete and categorical data.
method Subsampling-based method to test independence between cause and mechanism.
result Our method works for both discrete and categorical data without functional model assumptions.
New method learns exogenous variable distributions for better causal optimization.
problem Maximizing target variables in structural causal models.
method Learn exogenous variable distributions to improve surrogate models' fidelity.
result Improves approximation of structural causal models and broader application scenarios.
TS-K-means improves financial data clustering with dynamic time warping.
problem Inadequate handling of temporal dependencies in financial time series data.
method Integrates Dynamic Time Warping into Time Series K-means for financial data.
result TS-K-means outperforms traditional K-means in financial data analysis.
Causal approaches to fairness have seen substantial recent interest, both from the machine learning community and from wider parties interested in ethical prediction algorithms. In no small part, this has been due to the fact that causal models allow one to simultaneously leverage data and expert knowledge to remove di…
Novel framework for data sharing and coordinated exploration in concurrent RL with non-identical environments.
problem Learning more data-efficient and better policies in concurrent RL with non-identical environments.
method Proposes a novel algorithmic framework that leverages causal inference via ANM-MM to extract model parameters and a new data sharing scheme based on similarity measures.
result Demonstrates superior learning speeds on various tasks and effectiveness of diverse action selection.
FedDAG learns DAG structures from decentralized data.
problem Learning DAG structures from private decentralized data.
method Gradient-based framework with two-level structures.
result FedDAG learns DAG structure without direct data sharing.
The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X, Y. An example is to decide whether altitude ca…