Novel method uses information theory to measure causal influences during transient neural events.
problem Characterizing network interactions during transient neural events.
method Structural Causal Models, Information Theory, Transfer Entropy, Dynamic Causal Strength, Relative Dynamic Causal Strength.
result Introduced a novel measure, relative Dynamic Causal Strength, with theoretical and empirical support.
Study interpolating estimators for causal learning from observational data.
problem Learning causal models from observational data in complex model classes.
method Investigate min-norm interpolators and ridge-regularized regressors in a linearly confounded model.
result Interpolators cannot be optimal for causal learning under the principle of independent causal mechanisms, requiring stronger regularization.
Consistent estimator derived for confounding strength in observational data.
problem Estimating confounding strength in observational data is challenging due to unobserved confounders.
method Derived and adapted a consistent estimator using tools from random matrix theory.
result The original estimator is not consistent, but an adapted one is.
Dagma-DCE improves causal discovery with interpretable measures and open-source code.
problem Arbitrary proxy measures of causal strength in non-parametric causal discovery.
method Uses weighted adjacency matrices based on an interpretable measure of causal strength.
result Achieves state-of-the-art performance in simulated datasets.
Proposes a method to create robust linear models with noisy proxies of unobserved variables.
problem Learning robust linear models to handle interventions on unobserved variables with noisy proxies.
method Regularization term that balances in-distribution performance and robustness to interventions.
result Single proxy can create prediction optimal estimators under interventions of bounded strength.
A linear non-Gaussian structural equation model called LiNGAM is an identifiable model for exploratory causal analysis. Previous methods estimate a causal ordering of variables and their connection strengths based on a single dataset. However, in many application domains, data are obtained under different conditions, t…
While it is an important problem to identify the existence of causal associations between two components of a multivariate time series, a topic addressed in Runge et al. (2012), it is even more important to assess the strength of their association in a meaningful way. In the present article we focus on the problem of d…
Language models trained on chess board states outperform those on moves, even with causal masking.
problem Applying causal masking to spatial data for training unimodal language models.
method Trained bidirectional and causal self-attention models on both spatial (board-based) and sequential (move-based) chess data.
result Models trained on spatial board states achieve stronger playing strength than those trained on sequential data, even with causal masking.
IntDC framework uncovers causal relationships from non-interventional data.
problem Detecting causal relationships in non-interventional complex systems.
method Interventional Embedding Entropy (IEE) for causal strength measurement.
result IEE accurately finds causal edges and quantifies causal strength robustly.
The paper investigates causal relationships in heart failure prediction using machine learning.
problem Understanding the causal relationships between clinical variables and heart failure.
method Proposes a new computational framework for causal structure discovery (CSD) of mixed-type clinical variables for binary disease outcomes.
result Feature importance from nonlinear classifiers strongly correlates with causal strength of variables, but not differentiating cause and effect.
CausalMix generates synthetic data with causal controls for mixed-type tables.
problem Synthetic data for causal inference with mixed-type and multimodal tabular data.
method CausalMix combines Gaussian latent priors with data-type-specific decoders for control over overlap, confounding, and treatment effect heterogeneity.
result CausalMix achieves state-of-the-art distributional metrics and stable causal control.
Proposes a method to assess unobserved confounding effects in causal inference.
problem Assessing unobserved confounding in causal inference studies.
method Copula-based normalizing flows with sensitivity parameter ρ. result Estimates average causal effect (ACE) as a function of unobserved confounding strength.
New method combines strengths of two PCL approaches without density ratio estimation.
problem Estimating causal functions in Proxy Causal Learning with unobserved confounders and proxies.
method Kernel-based doubly robust estimators combining treatment and outcome bridges, density ratio-free.
result Outperforms existing methods on PCL benchmarks, including a prior doubly robust method.
We conduct an axiomatic study of the problem of estimating the strength of a known causal relationship between a pair of variables. We propose that an estimate of causal strength should be based on the conditional distribution of the effect given the cause (and not on the driving distribution of the cause), and study d…
Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typically used to model the data-generating process of variables. Recently, it was shown that use of non-Gaussianity identifies the full structur…
A new method uses randomized trials to estimate the strength of unobserved confounding.
problem Unobserved confounding compromises causal conclusions from non-randomized studies.
method Designs a statistical test to detect unobserved confounding strength and estimates a lower bound.
result Estimates an asymptotically valid lower bound on unobserved confounding strength.
Proposes ρ-GNF for sensitivity analysis of unobserved confounding.
problem Sensitivity analysis of unobserved confounding in observational studies.
method Copulas and normalizing flows to estimate average causal effect (ACE) as a function of unobserved confounding strength.
result Develops ρcurve to provide bounds for ACE and identify confounding strength required to nullify ACE. 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. In this paper, we study c…
This work explores the idea of a causal contextual multi-armed bandit approach to automated marketing, where we estimate and optimize the causal (incremental) effects. Focusing on causal effect leads to better return on investment (ROI) by targeting only the persuadable customers who wouldn't have taken the action orga…
New method combines experimental and observational data for causal inference.
problem Combining internal validity of experiments and larger sample sizes of observations.
method Empirical risk minimization (ERM) framework with cross-validation.
result Efficacy and reliability demonstrated on real and synthetic data.
Unified framework for causal inference under sample selection.
problem Causal inference under sample selection with treatment and outcome non-randomness.
method ForestRiesz estimator, Riesz representation framework.
result ForestRiesz estimator yields more stable treatment effect estimates than conventional double machine learning approaches.
Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factor…
Bounds on treatment effect sensitivity in causal reasoning using Hölder's inequality.
problem Estimating treatment effects in presence of unobserved confounders.
method Using Hölder's inequality, derived bounds on confounding bias based on unmeasured confounding strength.
result Bounds are tight under specific conditions of independence between U and T/Y.
Paper simplifies complex causal identifiability problems with exogenous isomorphism.
problem Achieving consistent answers to causal questions in Structural Causal Models.
method Introducing exogenous isomorphism and proposing ∼EI-identifiability. result Unified and generalized theories for practical applications in counterfactual reasoning.
Optimization algorithm CoCo improves causal inference from diverse data.
problem Identifying true causal relationships from data with spurious associations.
method CoCo optimizes for causal inference using environments with invariant causal relationships.
result CoCo provides more accurate causal estimates and predictions.
This paper provides a link between causal inference and machine learning techniques - specifically, Classification and Regression Trees (CART) - in observational studies where the receipt of the treatment is not randomized, but the assignment to the treatment can be assumed to be randomized (irregular assignment mechan…
New method uncovers small but significant local activities in time-series data.
problem Reconstructing small but important local activities in time-series data.
method Neural state-space models with latent causal-effect disentanglement.
result Demonstrated proof-of-concept on reconstructing ectopic foci in cardiac electrical propagation.
Regularization methods can overregulate, suppressing causal features.
problem Mitigating shortcuts in models exploiting spurious correlations.
method Analysis of regularization methods and their effects on causal features.
result Regularization can overregulate, suppressing causal features.
Study shows how financial report sentiment impacts bank profitability.
problem Understanding causal effects of financial report sentiment on bank profitability.
method Causal forest machine learning methodology, FinancialBERT sentiment scores, SHAP analysis, comprehensive dataset.
result Statistically significant causal associations between balance sheet and expense management variables and profitability.
A technique uncovers latent causal relationships in multiple time series data.
problem Identifying causal relationships in complex, dynamic systems.
method Blindly identifies latent sources by projecting observed data into pairs of components to maximize causality.
result Reveals multiple strong causal relationships not evident in observed data.
New model learns causal world dynamics from state space models.
problem Lack of causal world models in neural world modeling.
method State Space Models (SSM) with attention mechanisms.
result SSM can learn causal models of environments with equivalent performance.
Develops a framework for inferring causal relationships in networked data with uncertainty quantification.
problem Extracting reliable inference from complex Hawkes network data with uncertainty.
method Statistical inference framework based on maximum likelihood estimation and concentration inequalities of continuous-time martingales.
result Provides a non-asymptotic confidence set for uncertainty quantification.
DAG-WGAN learns causal structures using Wasserstein distance.
problem Learning causal structures from data with combinatorial challenges.
method Combines Wasserstein distance, auto-encoder, and acyclicity constraint.
result Demonstrates good performance compared to state-of-the-art models.
This paper addresses external validity bias in causal inference.
problem Estimating causal effects in a target population.
method Synthesis of approaches for generalizability and transportability, including tests for heterogeneity of treatment effects and differences between study and target populations.
result Framework for addressing external validity bias in causal inference.
New method identifies cause-effect relations in multivariate time series data.
problem Identifying cause-effect relations in multivariate time series data.
method Fictitious vector autoregressive model to identify long-run relations and causality strength.
result High accuracy in identifying true cause-effect relations in simulations and climate change analysis.
GRACE-C improves causal learning from time series data.
problem Misleading causal information due to mismatched timescales.
method Combines constraint programming with theoretical insights and prior information.
result Significantly faster and scalable causal learning for large datasets.
Sensitivity analysis for individualized effects in OTRs with binary risk factors.
problem Addressing omitted confounding in individualized effects of OTRs.
method Simulation-based sensitivity analysis to simulate unmeasured confounders.
result Benchmarking the strength of omitted confounding for binary risk factors.
Framework detects changes in causal dependence between variables.
problem Detecting changes in causal dependence between variables in the presence of confounders.
method Non-parametric approach using kernel mean embeddings and copulas.
result Proposed statistic accurately detects changes in causal dependence.
Forest-based methods estimate heterogeneous treatment effects, blending strengths for better performance.
problem Estimating heterogeneous treatment effects in randomized and observational studies.
method Causal forests and model-based forests, blending strengths for better performance.
result Local centering of treatment indicator and propensities is crucial for good performance in randomized trials.
Studies show that the representations learned by deep neural networks can be transferred to similar prediction tasks in other domains for which we do not have enough labeled data. However, as we transition to higher layers in the model, the representations become more task-specific and less generalizable. Recent resear…
Investigate the evolving structure of cryptocurrency interactions using high-frequency returns.
problem Evolution of cryptocurrency interactions
method Construct directed and weighted networks from Granger causal relationships between cryptocurrency log-returns.
result Normalized returns exhibit heavy-tailed distributions.
Explaining AI systems is fundamental both to the development of high performing models and to the trust placed in them by their users. The Shapley framework for explainability has strength in its general applicability combined with its precise, rigorous foundation: it provides a common, model-agnostic language for AI e…
Study identifies causal relationships without direct supervision from unknown interventions.
problem Identify causal relationships from unknown interventions without direct supervision.
method General nonparametric setting with multiple datasets from unknown interventions.
result Identify ground truth latents and causal graph up to ambiguities.
New method improves treatment effect estimation using autoencoders and causal bridge.
problem Inferring causal effects with unobserved confounders.
method Coupling autoencoder with causal bridge to estimate treatment effects.
result Improves accuracy of treatment effect estimates.
Develops Austen plots for assessing bias from unobserved confounding in observational studies.
problem Bias in causal estimates due to unobserved confounding.
method Formalizes confounding strength, uses Austen plots to visualize and quantify bias.
result Allows domain experts to assess the plausibility of strong confounders.
Study compares causal discovery methods for cyclic models with hidden confounders.
problem Detect causal directions in cyclic systems with hidden confounders.
method Comprehensive comparison of four causal discovery techniques.
result Performance varies across different experimental setups and dataset sizes.
Enhances optimization in multi-source settings with causal principles.
problem Optimizing functions with multiple sources of data and causal dependencies.
method Integrates Multi-Source Bayesian Optimization with Causal Bayesian Optimization principles.
result Improves optimization efficiency and reduces computational complexity.
Temporal Causal Prior-Data Fitted Networks (TCPFN) for industrial time series causal discovery
problem Estimating causal effects in industrial time series
method Temporal Causal Prior-Data Fitted Networks
result Zero-shot causal discovery with explicit reliability signals