A new framework solves the causal frame problem using potential levels.
problem How to make decisions based on relevant information without considering irrelevant details.
method Introducing Potential Level (PL) and proposing a PL-based Inference Framework (PLIF).
result PLIF is consistent with causal judgment findings and makes testable predictions.
Observed associations in a database may be due in whole or part to variations in unrecorded (latent) variables. Identifying such variables and their causal relationships with one another is a principal goal in many scientific and practical domains. Previous work shows that, given a partition of observed variables such …
Researchers prove NP-hardness of learning parameter-bounded Bayes nets.
problem Learning parameter-bounded Bayes nets is computationally hard.
method Proved NP-hardness of learning parameter-bounded Bayes nets and a promise search variant.
result Proved NP-hardness of a promise search variant of LEARN.
Derives fluctuation theorems and thermodynamic uncertainty relations for systems modeled as Bayes nets.
problem Entropy production in interacting systems modeled as Bayes nets.
method Derives fluctuation theorems and thermodynamic uncertainty relations for arbitrary sets and conditioned sets of systems in Bayes nets.
result Relates the entropy production of the overall system to the precisions of probability currents in individual systems.
New bounds explain deterministic non-smooth deep nets without large Lipschitz constants.
problem Challenges in explaining generalization of deterministic non-smooth deep nets.
method De-randomized PAC-Bayes margin bounds for deterministic non-convex and non-smooth predictors.
result New bounds avoid large Lipschitz constants, providing generalization guarantees.
New algorithms bound graph structure sampling and learning high-dimensional graphical models.
problem Learning high-dimensional graphical models and efficient graph structure sampling.
method Online learning framework with exponentially weighted average (EWA) or randomized weighted majority (RWM) forecasters using log loss function.
result New sample complexity bounds and efficient algorithms for learning Bayes nets, including trees and chordal skeletons.
PAC-Bayes with Backprop trains neural nets with competitive error estimates and tighter risk bounds.
problem Training probabilistic neural networks with PAC-Bayes bounds.
method Two training objectives derived from PAC-Bayes bounds, evaluated on MNIST and UCI data.
result Competitive test set error estimates and tighter risk bounds than previous results.
Empirical Bayes improves causal representation learning across multiple domains.
problem Estimating causal representations from data across multiple domains.
method Developed an EB f-modeling algorithm for linearly-mixed causal representations. result Our method achieves more accurate estimation of causal variables than other methods.
Oracle inequality for sparse neural nets adapts to unknown structure.
problem Sparse deep neural nets in nonparametric regression.
method Gibbs posterior distribution with Metropolis-adjusted Langevin algorithms and mixture of uniform priors.
result Oracle inequality showing adaptation to unknown regularity and structure, achieving minimax-optimal rate of convergence.
Paper introduces VBG for Bayesian causal structure and mechanism learning.
problem Bayesian causal structure learning with uncertainty over models.
method Variational Bayes-DAG-GFlowNet (VBG) method.
result VBG outperforms existing methods in modeling posterior over DAGs and mechanisms.
Paper proposes efficient algorithm for learning causal Bayesian networks using path queries.
problem Learning the exact structure of causal Bayesian networks from observational data.
method Polynomial time algorithm using interventional path queries to identify directed paths.
result Logarithmic sample complexity for learning transitive reduction of causal Bayesian networks.
BCD Nets use variational inference to estimate DAGs with uncertainty.
problem Uncertainty in inferring causal graphs from limited data.
method Variational inference framework for Bayesian DAG estimation.
result BCD Nets outperform maximum-likelihood methods in low data regimes.
Traditional approaches to Bayes net structure learning typically assume little regularity in graph structure other than sparseness. However, in many cases, we expect more systematicity: variables in real-world systems often group into classes that predict the kinds of probabilistic dependencies they participate in. Her…
Bayesian method tests Granger causality in functional time series.
problem Testing Granger causality between functional time series.
method Bayesian dynamic linear models (DLM) and Bayes Factor.
result Captures Granger causality between yield curves and weather conditions.
Bayesian Hierarchical Invariant Prediction refines ICP for better scalability and prior integration.
problem Improving computational scalability and invariance testing for causal inference.
method Bayesian Hierarchical structure to test invariance under heterogeneous data.
result Demonstrated improved scalability and potential as an alternative to ICP.
Improves model accuracy for neural nets in stochastic dynamics with partial prior knowledge.
problem Stability and accuracy in neural nets modeling stochastic dynamics with many parameters.
method Three steps: probabilistic weights, partial knowledge incorporation, and PAC-Bayesian training.
result Improved model fit with partial and noisy prior knowledge.
Bayesian model averaging improves causal effect estimation by averaging over multiple models.
problem Estimating causal effects under linear Structural Causal Models (SCMs).
method Bayesian model averaging using Gaussian scale mixture distributions for computational efficiency.
result Bayesian model averaging is optimal for causal effect estimation.
Paper shows identifiability of causal models with unobserved variables.
problem Identify latent variables in causal models with unobserved variables.
method Developed an autoencoding variational Bayes algorithm.
result Identifiability achieved with generalized faithfulness assumptions.
Bayesian method optimizes interventions for causal discovery.
problem Active interventions are needed for causal discovery when observational data is insufficient.
method Bayesian optimization-based approach using observational data and pre-experimental evaluation of interventions.
result Demonstrated effectiveness through various experiments.
SAM finds causal graphs from data without assuming a specific structure.
problem Discovering causal relationships from observational data without assuming a specific structure.
method SAM uses a game between neural nets and an adversary to estimate variable distributions and find the underlying causal structure.
result SAM successfully finds the underlying causal structure from observational data.
A new algorithm infers causal networks from data using topological thresholds.
problem Inferring causal networks from data.
method Two methods for determining topological thresholds: one to leave no disconnected nodes, the other to find a causal large connected component.
result The novel algorithm is faster and more accurate than the PC algorithm.
Bayes rule replaces do-calculus for causal inference.
problem Representing and addressing causal problems with probability theory.
method Encoding causal graphical models in Probabilistic graphical models and using Bayesian statistics.
result Causal effects can be estimated entirely within the Bayesian paradigm.
Seq-U-Net improves sequence modeling efficiency with dilated U-Net.
problem Efficiently modeling long-term dependencies in sequences.
method Causal U-Net architecture with dilated filters and slow feature hypothesis.
result Seq-U-Net achieves comparable performance with speed-ups of over 4x in audio generation.
Proposes CSG model to separate semantic and variation factors for OOD prediction.
problem Out-of-distribution examples cause conventional models to mix semantic and variation factors, leading to poor performance.
method Causal Semantic Generative model (CSG) based on causal reasoning, using variational Bayes for efficient learning and prediction.
result CSG can identify semantic factor and improve OOD prediction performance.
Automatic debiasing for causal and policy effects using Neural Nets and Random Forests.
problem Estimating causal and policy effects from high-dimensional or non-parametric regression functions.
method Automatic learning of Riesz representation using Neural Nets and Random Forests.
result Automatic debiasing method performs well compared to state-of-the-art algorithms.
Proposes Causal Loss to improve machine learning models' causal inference.
problem Machine learning algorithms often fail to capture causal relationships when data is inconsistent.
method Introduces Causal Loss, a model-agnostic loss function that enhances interventional capabilities.
result Causal Loss improves non-causal associative models to have interventional capabilities.
Bayesian methods can handle causal inference without needing do-calculus.
problem The challenge of representing and addressing causal problems using probability theory.
method Bayesian statistics and probabilistic graphical models.
result Causal effects can be estimated within the standard Bayesian paradigm.
Study examines how classifier performance is affected by training data quality.
problem How classifier performance is affected by training data quality.
method Extensive numerical experiments with four classifiers (Bayes, neural nets, partition models, random forests) on metagenomic assembly data.
result Classifier performance degrades as training data quality degrades, leading to breakdown-like behavior.
FSNN learns causal relationships in complex systems using neural nets.
problem Inferring causality in directed cyclic graphs.
method Constructs a non-linear system of ODEs using feed forward neural nets.
result Accurately models complex, non-linear systems with causal relationships.
Proposes MCTE framework for efficient imitation learning.
problem Efficiently learn sparse multi-modal policies from demonstrations.
method Maximum causal Tsallis entropy (MCTE) framework with sparsemax distribution.
result MCTEIL algorithm outperforms existing methods in simulations.
Bayesian method optimizes causal effect estimation from observational data.
problem Estimating causal effects from observational data with selection bias.
method Bayesian nonparametric approach using KL divergence and Fano's method.
result Optimal information rate achieved by a specific class of priors and an adaptation procedure.
Scientists develop a model to identify treatment responders from non-responders.
problem Analyzing samples that respond to treatment in studies.
method Causal two-groups (C2G) model, empirical Bayes procedures.
result The C2G model controls false discovery rate and has near-optimal power.
A machine learning approach optimizes data obfuscation for privacy.
problem Preserving data utility while protecting sensitive information.
method Generative adversarial networks (GAN) competition between generator and classifier.
result Optimal obfuscation mechanism reached through competitive nets.
Over the past two decades, several consistent procedures have been designed to infer causal conclusions from observational data. We prove that if the true causal network might be an arbitrary, linear Gaussian network or a discrete Bayes network, then every unambiguous causal conclusion produced by a consistent method f…
Single proxy variable helps estimate causal effects from confounders.
problem Estimating causal effects from treatment to outcome when unobserved confounders are present.
method Assumes a single, potentially multi-dimensional proxy variable of the unobserved confounder and a known mechanism generating the proxy from the confounder. Proves causal effects are identifiable under completeness assumption.
result Causal effects are identifiable under SPICE assumption.
A new method for estimating causal parameters from observables reduces the need for finite moment conditions.
problem Estimating causal parameters from observational data with unknown or infinite moment conditions.
method Variational Method of Moments (VMM) for a general class of estimators, including kernel and neural net-based methods.
result VMM estimators are consistent, asymptotically normal, and semiparametrically efficient.
Study improves paddy rice yield predictions in Peru using sparse regression and climatic variables.
problem Improving precision of paddy rice yield forecasts in Peru.
method Sparse regression, Elastic-Net regularization, climatic variables, dynamic transformations.
result Improved predictive performance of paddy rice yield forecasts.
Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponential number of possible directed, acyclic graphs. When the prior is decomposable, two classes of graphs where efficient learning can take plac…
Hierarchical Bayesian networks and neural networks with stochastic hidden units are commonly perceived as two separate types of models. We show that either of these types of models can often be transformed into an instance of the other, by switching between centered and differentiable non-centered parameterizations of …
New method for causal effect estimation with hidden confounders.
problem Estimating causal effects in the presence of hidden confounders.
method Singular value decomposition of a conditional expectation operator followed by saddle-point optimization.
result Our method outperforms existing methods on common benchmarks.
New method balances deep covariates for causal inference using adversarial training.
problem Balancing covariates for causal inference from complex data.
method Adversarial training of a weighting and discriminator network.
result Effective handling of complex relationships and image confounders.
HyperST-Net uses hypernetworks to improve spatio-temporal forecasting.
problem Forecasting spatio-temporal data is challenging due to complex spatial and temporal factors.
method Proposes a framework based on hypernetworks with three modules: spatial, temporal, and deduction.
result Models achieve significant improvements over state-of-the-art baselines.
Novel approach constructs differential causal networks from EEG data.
problem Difficulty in modeling interactions of thousands of neurons in group comparisons.
method Hierarchical differential dynamic causal nets based on Chen-Fliess expansions.
result Evidence of network functional disruptions in epileptic brains.
A novel Bayesian computation method using importance weighting improves numerical stability and performance.
problem Bayesian computation stability and performance issues.
method Nonparametric approach via feature means, importance weighting, and kernel Bayes' rule.
result Importance weighted kernel Bayes' rule yields superior numerical stability and performance.
A novel method for learning DAGs from positive-valued data.
problem Causal discovery from observational data of positive-valued variables.
method Hybrid Moment-Ratio Scoring (H-MRS) algorithm combining moment-based scoring and log-scale regression.
result H-MRS integrates log-scale Ridge regression for moment-ratio estimation with a greedy ordering procedure based on raw-scale moment ratios, followed by Elastic Net-based parent selection.
Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise. To alleviate the problem of spurious causality, Haufe et al. (2013) proposed to contrast measures of information flow obtained on the original data against the same measures obtained on time-rever…
FOCUS improves offline RL by incorporating causal structure into world-models.
problem Learning effective policies from historical data without interaction.
method FOCUS proposes a practical algorithm that learns and leverages causal structure in offline RL.
result FOCUS outperforms plain model-based offline RL algorithms and other causal model-based RL algorithms.
A new process-level model explains how humans detect dependencies quickly.
problem Understanding how humans handle probabilistic independence relations efficiently.
method Developed a rational, distributed, message-passing model called D*.
result D* shows a tendency to quickly detect dependencies, outperforming other algorithms.